Beverage sales are spiking in 2023, up to 35% year-on-year in February alone, and fleets need to be prepared for a busy summer of deliveries.
On-time in full (OTIF) is a key variable in beverage sales. Like most fast-moving consumer goods, beverage deliveries need 100% OTIF, day in and day out, to prevent losing revenue and market share to competitors whose private and dedicated fleets can better supply retail customers.
In April, alcohol and beverages were out-of-stock 13% on average, which amounts to a revenue loss of $1.12 billion. Beverage sales, therefore, are inextricably tied to fleet uptime and efficiency. To improve these critical measures, some of the most successful name-brand fleets are using pre-built predictive models to monitor a wide range of vehicle and engine-failure types and make proactive repairs.
The pre-built models harness the power of data science and cloud computing to identify maintenance needs weeks in advance of failure. In many instances, the insights from the models identify pending issues before fault codes appear. With these insights, fleets no longer need to rely on fault codes or static mileage and time-based intervals to inspect vehicles and schedule repairs.
Accurately diagnosing vehicle and engine failures at the earliest possible stage holds the key for beverage fleets to achieve greater uptime and availability of assets. With predictive insights, repairs can be scheduled and completed between shifts to keep vehicles operating in the normal rotation throughout the peak summer season.
Data as the competitive advantage
Accuracy is the linchpin of effective predictive maintenance and workflow automation. Pre-built models that identify the root cause of a pending failure, with a very low margin of error, make it possible to standardize diagnosis and repair processes and improve shop efficiency so technicians can do jobs correctly the first time.
By combining accurate insights with a standardized repair process and system integrations, beverage fleets can quickly move beyond these all-too-common scenarios:
● A driver returns from a shift, enters the shop, and tells a technician about a mechanical issue. “I heard a knock in the engine and the vehicle does not have enough power.” The technician connects a scanner to the vehicle. Diagnosing the problem takes time and the results are often inconclusive.
● The maintenance department is overwhelmed by data from vehicle-telematics systems that send hundreds, and perhaps thousands, of fault codes every day.
● Full-time employees are creating spreadsheets with pivot tables to organize fault codes and vehicle data by severity and location. The same employees are manually creating work orders and emailing their documents to technicians.
The result is a more streamlined process so drivers can focus on what matters most to beverage brands: keeping shelves stocked across the country.
Automate work-order processes
Predictive models that integrate with telematics and maintenance-software systems will create a direct path from diagnosis to repairs, greatly improving the speed efficiency of shop resources.
When using predictive models, private and dedicated fleets can maximize utilization of technicians and shop resources by following three practices:
- Train dedicated work-order experts. Designate a team to manage vehicle insights and teach technicians to use the information to complete work orders correctly and efficiently.
- Reduce steps for repairs. Create a standard repair procedure for each predictive insight. For instance, you could document repair procedures for your top 10 issues and create work orders immediately when insights are received by email or directly into your fleet-maintenance system through an API.
- Maximize technician efficiency. To optimize work for technicians, some fleets provide large monitors in shops that list inbound work. This gives technicians real-time information to prepare for vehicle arrivals and expedite repairs.
Work-order automation is possible with system integration between predictive models, fleet telematics, and maintenance-management software. When technicians close out work orders in the maintenance program, the integration feeds the algorithms of predictive models with repair data to continually learn and improve the accuracy of results.
Staying one step ahead of fault codes
Modern vehicles and engines have numerous failure points. In many cases, the signs of failure appear before fault codes. After-treatment systems are among the most serious failure types. Predictive models can identify the warning signs to prevent roadside breakdowns and expensive repairs.
Beverage-delivery operations are prone to after-treatment system failures. Engines may not be reaching high enough RPMs and operating temperatures to initiate passive or rolling “regens” to clean the diesel-particulate filter (DPF). Knowing this, beverage fleets often have drivers or technicians do a manual regen while trucks are parked at terminals.
If manual regens are not cleaning the DPF completely, the vehicle could unexpectedly derate its power or shut down completely on the road. By monitoring engine data, a predictive model can identify problems, such as a stuck valve that is limiting the release of diesel-exhaust fluid (DEF), before it’s too late.
Fleets can use predictive insights to address the root cause of after-treatment system problems weeks before failures. Other types of failures that pre-built models can identify include EGR coolers, low oil pressure, NOx sensors and more.
Demonstrating ROI
The return on investment from a predictive-modeling solution comes from the cost savings of minimizing downtime. By avoiding unplanned maintenance events and road calls, beverage companies can save more than $600 per vehicle per day.
With predictive insights, fleets can also save by completing more repairs in-house and avoiding the extra cost and downtime of dealerships. Additional ROI is created by increasing revenue from higher vehicle availability and by fuel savings from better vehicle performance.
As demand for beverage products skyrocket in the coming months, implementing a predictive fleet-maintenance strategy will be a key differentiator for brands looking to make the most of the busy summer season.
