The world’s most sophisticated manufacturers are exploiting Big Data and Analytics to drill ever deeper into their operations for new cost efficiencies.
One area of data being mined to new depths is material handling—specifically the utilization of forklifts and other industrial trucks. And no wonder: the costs associated with a forklift—buying it, maintaining it, fueling it, repairing damage from accidents, and paying operator wages and benefits across multiple shifts—can add up to well over $200,000 per vehicle per year.
Here are three ways that Forklift Analytics—like I.D. Systems’ PowerFleet IQ™ platform—are helping manufacturers reduce costs and rethink their industrial engineering standards.
1. Fleet activity analysis and reduction of equipment and labor costs. One world-class manufacturer uses Forklift Analytics to identify gaps in expected vs. actual lift truck usage. As a result, the company has been able to eliminate 7% of its fleet, cut labor costs by over $2 million per year, and identify other opportunities for even more cost savings.
First, Forklift Analytics can identify underperforming plants at the enterprise level. For instance, heat maps, like the one below, show how each plant compares to corporate averages—and other plants—in terms of forklift fleet productivity (measured by time the vehicles were actually in motion) and utilization (the time in a given period the vehicles were used).
To normalize data across sites, regardless of fleet size, Forklift Analytics analyze percentages and ratios as well as absolute numbers.
Next, Forklift Analytics can identify gaps of activity at each site—focusing especially on underperforming sites identified in the enterprise view. For example, the chart below shows the peak number of forklifts active at the same time (that is, in simultaneous motion), day by day, compared to the total quantity of vehicles available in the fleet.
The gap between the number of vehicles available and the number used simultaneously at any given time represents a potential opportunity to reduce the fleet and reallocate operators.
In the example below, out of a fleet of over 150 lift trucks, only about 100 are ever being used at the same time on any given day.
But which vehicles should be eliminated from the fleet? Forklift Analytics can help provide that answer, too. The following heat map identifies specific vehicles at one plant that are used well below the fleet average.
A manager can drill down to look at detailed vehicle usage data with the click of a mouse.
Of course, in some cases, low usage reflects specialized equipment needed only occasionally for specific tasks. Management may not be able to eliminate those types of vehicles. But often the low-use vehicles are common types, easily retired from the fleet.
Another way to analyze forklift usage at the site level is by operational departments—shipping/receiving, production, maintenance, etc. This type of data is especially useful to Industrial Engineers who need to understand how much time common activities actually take, compared to job plans.
The chart below, for example, shows the forklift login activity of one group of 45 lift truck operators, all assigned to the same department. During the period reported, as many as 40 operators were logged into equipment at peak times—but the average number of operators logged into their trucks was no more than 25!
This suggests that at least 5 operators can be reassigned from this group. And if workloads could be better balanced, two-to-three times that number of drivers could potentially be reallocated!
As with vehicle data, Forklift Analytics enables users to drill down into specific operator utilization details to determine the most—and least—productive drivers.
2. Safety metrics, culture change, and damage cost reduction. Forklift Analytics help senior management apply the best practices of forklift telematics systems (like I.D. Systems’ industry-leading PowerFleet®) throughout their enterprise. Organizations with large fleets of lift trucks across multiple sites can routinely reduce damage costs by 60% – 90%, in many cases saving millions of dollars.
Forklift Analytics offer key performance indicators (KPIs) for multiple forklift safety metrics, including:
– Rate of impact events per vehicle motion time
– Rate of vehicle lockouts due to critical safety issues
– Rate of vehicle lockouts due to failure of operators to complete their pre-shift safety checklists
For instance, in the KPI dashboard below, this tab shows a snapshot of 7-day rankings and 30-day trends for each site, based on the rate of high and severe impacts incurred by each site’s forklift fleet. Green dots confirm compliance with corporate standards; yellow dots indicate improvement is needed; red dots flag an unacceptable performance. (Thankfully, this particular company did not have any unacceptable sites that week.)
With safety KPIs, as with productivity dashboards, Forklift Analytics allow users to drill down from an enterprise-wide view, through specific site data, all the way into detailed statistics on individual forklift operators. This allows rapid analysis and decision-making: corporate leadership can identify the safest and least-safe forklift fleets; plant managers can compare their sites to corporate benchmarks; and floor supervisors can see which forklift operators are model drivers—and which need more training or discipline.
This type of data typically enables—and sustains—a fundamental change in safety culture and a significant reduction in accidents and damage costs.
3. Maintenance efficiency and lower maintenance costs. Forklift telematics systems like PowerFleet® give maintenance departments control over and visibility of industrial trucks—vital to managing large fleets. Forklift Analytics takes maintenance management to another level. For organizations that perform their own forklift maintenance, this can mean significant improvements in labor efficiency and a high double-digit reduction of preventive maintenance (PM) costs.
Forklift fleet management technology provides four keys to improving maintenance efficiency:
– Alerting maintenance in real time when pre-shift checklists flag maintenance issues
– Tracking and planning PMs according to true vehicle motion time, not key or calendar time
– Locking out equipment that needs repair or is overdue for PM
– Seeing the location of equipment on a facility map, so it can be retrieved quickly (especially important in large plants)
Forklift Analytics accumulates and analyzes maintenance-related data across the entire enterprise. This helps establish fleet-wide benchmarks that put site-by-site performance into perspective, revealing the most—and least— efficient maintenance practices.
Corporate managers can analyze high-performing sites to see what works, and apply those best practices to other sites. Plant managers at low-performing sites can investigate quickly, in detail, and identify ways to improve.
For a simple example, let’s say Plant A performs PMs every 250 motion hours while Plant B does them every 300 hours. If both plants have about the same rate of unplanned maintenance, Plant A should change its PM routine to every 300 hours, saving about 17% of the time and money its spends on routine PMs.
Forklift Analytics can also help quantify and project maintenance cost savings based on telematics data. For instance, the dashboard example below shows site-by-site PM reductions and associated cost savings when PMs are based on actual motion-hours compared to key (login) time.
Productivity improvements…leading to forklift fleet reduction and operator labor cost savings.
Safety metrics…leading to a stronger safety culture and lower forklift accident costs.
Maintenance data and best practices…leading to lower forklift repair costs.
These are the top three ways manufacturers are getting big benefits from the Big Data of Forklift Analytics.
For more details about I.D. Systems’ PowerFleet IQ™ Forklift Analytics platform, including even more ways it can mine data for cost savings and productivity improvements, visit our web pages on…
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