We are working with a leading specialist provider in the distribution and logistics space. They provide delivery to auto dealers for the dealers’ parts and services business.
This specialization allows them to be more efficient and knowledgeable with regard to their customers and the parts they are delivering. Seeing that these parts cannot be sold and services cannot be scheduled and performed until the parts are delivered, it is exceptionally important that they deliver on time and with complete deliveries.
The problem this organization faced was knowing which routes were responsible for delayed deliveries and what was causing the delays. The company felt their routes and stopping points caused inefficiency, but they also wanted to forecast the impact of extraneous factors such as weather, road construction, and traffic patterns. With this information, they could adjust schedules to proactively service their customers, adjust delivery times, and gain greater efficiencies from their fleet. In addition, they wanted to incorporate the inventories from their parts suppliers and their geographic locations so they could minimize empty return trip trucks and duplicate runs as well as minimize the inventory that they had to keep on hand in their distribution centers while knowing which suppliers had what parts in stock.
With 10 distribution centers, each with over 100 routes, and each route with over 15 stops, this is a significant logistics problem. Add to that over 2,500 parts from over 200 manufacturers and this becomes a significant supply chain opportunity. In addition, each truck itself, over 1,000 of them, is a constant data generator, capturing location, efficiency, speed, right turns, left turns, stop times and length of stop. How do you coordinate all of this data against the business issue of on-time deliveries with the greatest efficiency?
Our customer chose Halo. Using our data warehouse automation solution, they first aggregated their enterprise inventory and logistics data with two external data feeds. Then they created a series of operating metrics to be used by managers as well as KPIs for reporting to executives. An important feature of these reports were predictive metrics. These metrics, created using the predictive model tool in Halo, identify routes with risk factors and suggest the likelihood of one or multiple delays in the near future (weeks). Lastly, they distribute these efficiency reports and interactive dashboards using Halo’s data visualization and collaboration platform. An important feature of this approach was deploying mobile versions of each report for traveling executives.
Today, everyone from the CEO down to the route dispatcher at each distribution center knows where the issues are and what needs to be done to correct them. It also allows our customer to be much more proactive in dealing with both the delivery points and their suppliers. As orders and inventory stocks can be readily shared, increasing parts production efficiency at the manufacturer and delayed delivery points can be more readily notified so they can more effectively reschedule their work loads. First year savings from reduced inventory and greater fleet efficiencies are estimated at over $3 million.