Challenge: In a Hyper-competitive Market, Do More with What You Have to Cut Costs
Five years ago, a leading multinational food & beverage company deployed Powerfleet’s® forklift telematics system to improve material handling safety. The project was a big success. Based on its initial return on investment (ROI), the Company rolled out Powerfleet on more than 2,500 pieces of material handling equipment across almost 100 of its food and beverage manufacturing and distribution sites in North America.
But with more than $60 million invested in its material handling fleet—and a labor cost of over $200 million to run that fleet—the Customer wanted to find out if Powerfleet could save it even more money.
This Company has a long history of using cutting-edge supply chain technology. For example, it is a leader in blockchain technology for sharing data securely across the food supply chain to help ensure food safety. The Company’s Center of Excellence (CoE) focuses on continuous improvement in manufacturing and distribution, with data as its life blood.
To find new ways to reduce material handling costs, one of the key data sets the CoE wanted to understand better was the relationship between lift truck safety and productivity.
With Powerfleet forklift telematics already in place across the Company’s entire fleet of lift trucks, the CoE didn’t have to look far to find the data-driven solution it needed.
Solution: Powerfleet IQ™ Forklift Analytics
To gain deeper insight into the cost of lift truck safety vs. operator efficiency vs. material handling velocity, the Company licensed Powerfleet IQ Analytics, a Powerfleet software option, to link data from multiple sources.
Specifically, the Company wanted to use Powerfleet IQ to blend these data ingredients:
Before launching a new technology for its material handling workforce, the Company knew from experience it needed to plan carefully and be proactive about the way it would use the data.
One of the pitfalls of data-driven technology, like Powerfleet IQ Analytics, is that the data can be overwhelming. Another challenge is translating the data into action. All too often, a flood of data is received passively, without a clear vision of what to look for or how to react.
For any new system to succeed, the people who will get the data need to know ahead of time what they will be looking for—and exactly what they are supposed to do with it.
The Company knew that for Powerfleet IQ to be most effective, key employees—lift truck operators, line supervisors, facility managers, and corporate management—needed to care about the outcome. The Company had to make more than a financial investment; it had to engage and motivate these stakeholders.
The key to making workers care is accountability. So the Company devised a “carrot-and-stick” plan with both incentives for “good” behaviors and consequences for “bad” behaviors. Using Powerfleet IQ Analytics data, the Company:
Results: Deep Data Insights Cut Forklift Damage 85% while Meeting/Exceeding Pallet-Move Goals
To better measure what sites and people needed a better balance of material handling productivity and safety, Powerfleet worked closely with the Company to integrate its Kronos® time-card system and SAP® warehouse management system (WMS) with Powerfleet IQ Forklift Analytics. This created a whole new set of integrated data points, such as:
An excerpt of the latter type of chart is shown below, with the data aggregated by site. In this example, the “Demarest” site moves almost as many pallets as “Cresskill,” but with about half as many moderate and medium impact events. The user can click on any site to drill down into individual driver performance at that site, to determine which operators were at the root of the data.
Powerfleet and the Customer also built KPI scorecards, so all the critical metrics could be digested in one quick bite. The following examples focus on, respectively, forklift fleet safety and productivity, by site. In the KPI scorecard excerpted below, the screen shows the 7-day safety rankings and 30-day trends of each site, based on their forklift fleets’ rate of high and severe impacts. Green dots indicate compliance with corporate standards; yellow dots suggest improvement is needed. (Red dots would indicate a site’s performance is unacceptable.)
The next KPI scorecard excerpt ranks sites by forklift fleet efficiency, based on the amount of time vehicles spend in motion compared to the time vehicle operators are logged into the equipment. The “Demarest” site makes the most efficient use of its equipment—it has the highest ratio of motion (time vehicles are moving) to login (time drivers are logged in)—even though the site is average in its total hours of equipment usage. Conversely, although “Montvale” seems to be among the busiest sites, in terms of total hours of equipment usage, it is actually the lowest-performing in its ratio of vehicle motion time to driver login time.
“Powerfleet IQ Analytics drove much safer lift truck operations without a loss of material velocity: 85% less forklift damage and 100% of target pallet moves … for over $2 million in cost savings across the enterprise.”
The first key role of Powerfleet IQ was to validate data accuracy. One KPI scorecard monitored the health of system components—particularly to confirm that the Powerfleet vehicle-mounted hardware was collecting data normally and attributing it to identified operators. With validated data flowing smoothly, the Company went through a period of analysis over several months, without acting on the data. This enabled the Company to set expected benchmarks without rushing to judgment about individual operator performance. Importantly, the established benchmarks were ratios (not absolute numbers) to normalize performance across operators, vehicle types, and sites with different operating patterns.
Also significantly, Powerfleet IQ enabled different standards to be set for different vehicle types, and accounted for drivers who operated different types of vehicles in same shift or day.
Based on these benchmark ratios, the Company set up multiple KPI scorecards for different stakeholders: individual drivers (with daily, weekly, and monthly metrics); supervisors (focused on all drivers each shift); warehouse managers (with a view of the entire facility); and corporate management (with stack-ranking of all sites in a single view).
This data hierarchy enabled a rapid, logical flow of information and decisions. Corporate management identified high- and low-performing sites. Facility managers saw where they ranked compared to other sites, and where they stood in relation to the target benchmarks. Line supervisors drilled down to understand which forklift operators were high achievers—and which needed refresher training. And forklift operators could see in black-and-white where they stood.
In the end, the food & beverage Company was able to drive much safer lift truck operations while maintaining high material velocity through its distribution system. The result was an 85% reduction in forklift damage costs with 100% achievement of target pallet moves.
Why Did Powerfleet IQ Analytics work so well?
“We worked with Powerfleet to integrate data from several systems and set up new benchmarks for safety and productivity. We validated the data, then analyzed it to identify specific opportunities to improve. By focusing on the sites and people that needed the most improvement, we were able to reduce forklift damage costs 85% while maintaining—or exceeding—our goals for pallet moves.”
— National Warehouse Manager
Conclusion: New Ways of Combining and Analyzing Lift Truck Data Can Radically Improve Material Handling Operations
Using Powerfleet IQ forklift analytics, this food and beverage Company was able to change the way it measured and balanced productivity (pallets moved) with safety (forklift collisions) and efficiency (active time on equipment vs. paid time). This data enabled a new approach to material handling discipline, with high-performers rewarded with financial and other incentives, and low-performers trained to improve. The bottom line was a stronger safety culture, with a new way to measure and manage productivity.