Senior Fellow and Director of Analytics
Driving success from the manufacturing plant to the open road
Navistar uses analytics to improve talent retention, streamline the supply chain and help its customers keep on trucking
Trucks are the lifeblood of US commerce, transporting more than 70 percent of all freight nationwide. Without these vehicles circulating goods near and far, trade would slow to a trickle.
To keep its customers’ trucks going for the long haul, Navistar has made “driven by uptime” its motto. “We want to build the trucks that stay on the road the longest,” says Gyasi Dapaa, Senior Fellow and Director of Analytics for Navistar, a leading manufacturer of commercial trucks, buses, defense vehicles and engines. “And that mission drives what we do every day.”
We want to build the trucks that stay on the road the longest, and that mission drives what we do every day. Ultimately, SAS helps us deliver on our promise of being the ‘uptime company.’
The analytics team at Navistar supports this mission by weaving sophisticated predictive analytics into every aspect of the enterprise’s culture. Navistar uses SAS® Office Analytics to optimize many operations – from human resources to supply chain to sales.
Optimizing talent retention and recruitment
Achieving Navistar’s goal starts with recruiting and retaining the best people who can produce the best trucks. Dapaa’s team of data scientists works with the human resources department, using analytics to understand what drives employees – and how to find and retain them.
“Our main focus is to make sure that each and every business unit of Navistar is able to make the best decisions using data,” Dapaa says. “If we help our HR department to recruit and retain the best talent for Navistar – whether they are the best engineers, the best supply chain thinkers or the best procurement folks – we can produce trucks that beat the competition.”
And they didn’t stop there. The team also developed a data-driven, evidence-based model called Talent Quadrants (Tal Quad). An advanced Tal Quad plots performance on one axis and the probability of retention or termination on the other axis to determine who is at most risk of leaving. This information allows HR to take action to retain them.
“For example, we’re able to see that an employee is a superstar, but he has a 50 percent chance of leaving, so let’s do something about it,” explains Dapaa.
Streamlining the supply chain
As a manufacturer, Navistar needs to optimize the number of units it builds in each plant. Each plant relies on multiple suppliers for parts. Plants must balance fast response to customers with giving suppliers enough time to meet demand to minimize cost of expedited shipments for necessary parts.
SAS Office Analytics allows Navistar to calculate the optimum lead times and plant capacity rates given various constraints. First, the analytics team looks at the cost of expedited shipments with different supplier lead times (e.g., two weeks, three weeks and seven weeks). Next, it analyzes the relationship between lead times and the frequency of “red tags,” which are trucks that must be rolled off the assembly line to wait for a part. Another element is the plant’s ability to meet customer deadlines given existing capacity and the impact on customer loyalty.
“We started with six sets of data: supplier data, production floor data, truck build data, expedited freight data, logistics data and delivery data,” Dapaa explains. “Then using SAS, we cleansed the data, reduced the optimization problem to a disparate set of regression analytics problems and figured out correlations between the ‘line set’ time and various supply chain metrics. Then we brought everything together to paint a coherent picture of the optimal lead time and capacity rate.”
The bottom line: Navistar wants to have the lowest cost tied to getting its parts from suppliers while reducing missed deliveries. “We try to correlate and predict factors related to costs to build versus satisfying customer demand for the delivery of new trucks within a certain time frame,” Dapaa says. “After all, shipping new trucks on time helps keep our business running because we, in turn, are keeping our customers’ businesses running. This leads to happy customers, which drives loyalty.”
Balancing high-value sales territories
When it comes to keeping the sales force happy, the challenge is making sure that sales territories are equitable. One of Navistar’s business units consists of 30 sales managers who work with about 4,000 dealers. Finding a way to divvy up this business among the sales managers requires a constant balance of multiple pieces of data, such as the dealer’s proximity to assigned sales manager and the equity of each sales territory.
The analytics team took advantage of SAS optimization to create individual sales territories that offered revenue opportunities balanced with manageable geographic areas. The team also adjusted the number of dealers assigned to each sales manager, helping these managers learn and apply best practices across dealers.
Comprehensive analytics everywhere
As Navistar incorporates more analytics into the production and delivery of its products, it is achieving better performance in the marketplace. Solving HR, territory assignment, supply chain optimization and other challenges requires an ecosystem designed to churn out insights. In meeting these challenges, Navistar is now armed with a team of talented data scientists, many terabytes of data, and a single integrated SAS solution that enables it to separate useful data from noise, and thereby extract insights.
“Overall, SAS has been a great tool for us,” Dapaa says. “It helps us ensure our dealers are assigned to their sales managers in the most optimal manner; gives us a predictive way to maximize the production and delivery of newly produced trucks; and guides our decisions on who to hire and invest in. Ultimately, SAS helps us deliver on our promise of being the ‘uptime company.’”
Photos courtesy of Navistar.
- Understand key drivers of employee retention and churn to better recruit and retain top performers.
- Improve the delivery timing with the lowest cost structure supporting the shipment of newly produced trucks.
- Assign an optimal number of dealers to internal sales and service teams to maximize revenue opportunities.
- Improved employee retention and loyalty.
- Optimized manufacturing lead times and plant capacity rates.
- Enhanced sales territory allocation so sales opportunities and travel times to dealers are divided equitably across sales managers.
- Ensured sales managers served enough dealers to benefit from economies of scale without becoming overburdened.