If a farmer’s tractor breaks down during harvest or a courier’s van has engine issues, they can’t afford to wait long for spare parts to arrive—they’ve got a job to do. FleetPride is transforming its supply chain management with analytics, helping to ensure customers get the parts they need, when they need them.
To help get vital spare parts to customers on time, every time, FleetPride needs its extensive supply chain to run like a well-oiled machine. How could it maintain smooth supply chain operations?
FleetPride deployed descriptive, predictive and prescriptive analytics solutions from IBM, giving supply chain managers game-changing insights into operations.
- Helps design smarter distribution networks that optimize speed and costs
- Accelerates inventory movement, reducing labor costs and increasing revenues
- 99.5% of warehouse packing tasks are now error-free
Business Challenge Story
From tractors and delivery vans to excavators and garbage trucks, countless businesses depend on heavy-duty vehicles to get their jobs done. When these vehicles suffer mechanical issues, these companies can’t afford to wait too long for spare parts to arrive—which is why so many turn to FleetPride.
Homarjun Agrahari, Director, Advanced Analytics at FleetPride, takes up the story: “The ability to get spare parts out to our customers rapidly is what sets us apart from our competitors. You can buy the parts we sell at any store. The reason we’re number one in the heavy-duty aftermarket channel is that we are committed to getting parts to customers when they need them, wherever they are in the country, at the right price.”
Managing five regional distribution centers, FleetPride operates over 260 locations across the United States. “Our extensive distribution network and integrated supply chain are absolutely critical to our success,” says Homarjun Agrahari. “But keeping such a large, complex supply chain running smoothly is a constant challenge.”
He explains: “A weak link can jeopardize the entire chain, dragging down overall efficiency and impacting customer service, so it’s vital we constantly look to optimize our warehouse and distribution processes. Previously, our managers had no standardized criteria by which to measure the effectiveness of any changes they made to operating strategy—they had to rely on experience and instinct.
“To improve the efficiency of the entire supply chain, we wanted to take the emotion out of strategic decision-making and let data do the talking. However, until recently, we lacked the in-house skills and the proper tools to access our operational data and turn it into insight.”
Shifting gear with cutting-edge data analytics
To help drive its decision-making, FleetPride knew it needed to harness operational data from its warehouses and logistics network. The company implemented a suite of IBM Analytics solutions, including IBM® Cognos® Analytics, IBM Planning Analytics (formerly known as IBM Cognos TM1®), IBM SPSS® Modeler and IBM ILOG® CPLEX® Optimization Studio.
As a first step, FleetPride used IBM Cognos Analytics to design and distribute daily warehouse stock and inventory reports. This gives warehouse managers a comprehensive overview of the level and location of stock, and shows recommendations on where to store each type of item based on customer demand. Armed with this insight, managers can make sure that the most popular items are stored near the shipping dock—saving a great deal of time for warehouse staff and increasing productivity.
Next, FleetPride used IBM SPSS Modeler to build a model that uses three years of historical shipping data to predict the number of in- and outbound shipping orders per warehouse, over daily, weekly and monthly horizons. This makes it much easier for warehouse managers to adjust their labor planning and maintain the right level of staffing to deal with customer demand on any given day.
The company also built an optimization model of its entire distribution network with IBM ILOG CPLEX Optimization Studio.
Homarjun Agrahari adds: “CPLEX can help us work out the optimal locations where we could position a warehouse to minimize delivery time and costs across the entire network. When we’re deciding whether to build or acquire new warehouses, this kind of insight can make a significant difference to our network design strategy.”
Haoming (Ryan) Tian adds: “Here at FleetPride, we leverage multiple analytics technologies in an integrated methodology. IBM Cognos Analytics acts as an enterprise reporting platform so business stakeholders can quickly access and evaluate their business stats.
“We use IBM SPSS Modeler as a data mining and predictive technology to generate statistical models that can drive future insights for the business and help solve the challenges identified during descriptive analysis.
“Finally, CPLEX plays a dual role of integrating with IBM SPSS to derive optimal business suggestions based on statistical modeling under real world constraints, and also working independently to solve other optimization problems.”
Revving up supply chain efficiency
With the IBM Analytics solutions in place, FleetPride has gained unprecedented insight into operational data—transforming its approach to supply chain management.
Homarjun Agrahari notes: “We no longer need to rely on hunches or best guesses to manage all the links in the supply chain. Our managers all have quick, easy access to the latest operational data via detailed reports that help them make better-informed decisions to improve the efficiency of the entire supply chain.
“For example, daily stock reports have enabled warehouse managers to optimize the layout of the products, reducing the amount of time employees spend walking around the different storage areas empty-handed. This helps to make the entire warehouse more efficient and productive.
“The shipping prediction model we designed in SPSS Modeler, meanwhile, has helped us to manage staffing levels much more accurately, helping to ensure that we only bring in as many people as we need.
“Knowing approximately how many staff we need to process in- and outbound shipping on any given day has also helped to reduce the number of employees working overtime. Spending on overtime was a significant expense in the past. Now that we’re able to plan staffing levels more accurately, overtime spend is negligible and we’ve been able to make substantial savings.”
He adds: “Another model we designed in SPSS Modeler predicts the likelihood of warehouse staff making picking mistakes, which has enabled managers to take measures to simplify product labeling—now 99.5 percent of packing is error-free. This is an especially valuable insight, as many of the components we offer come in different sizes or configurations, and the smallest dispatch error could prevent a customer from getting their vehicle repaired quickly.”
Armed with indisputable data-driven insight into operations, FleetPride is better-positioned than ever before to keep its supply chain running efficiently.
Homarjun Agrahari concludes: “We’re highly confident that we can keep improving supply chain performance even as our distribution network grows in sophistication. Harnessing advanced analytics software helps us deliver parts to our customers on time—which helps them keep their vehicles on the road and their businesses running smoothly.”