Automotive logistics is immersed in big data. Consider the sources. There are 14 OEMs in North America, with 76 plants planned or currently in operation utilizing 280,000 parts for 160 models. Among some 400 suppliers, there are 5,000 plants. Add to the equation the 17,500 U.S. dealerships and 150 dealership groups slated to sell some seventeen million vehicles this year.
Between OEMs, suppliers, dealers, carriers, logistics service providers, and other third-party providers, that’s countless millions of pieces of data across the automotive ecosystem.
The numbers can be intimidating, or can be an opportunity. It depends on how organizations are prepared to manage the inevitable data flow.
The intelligent use of big data can pave the road to success, whether that’s the right part or car in the right place at the right time with the right quality at the right cost; the elimination of waste; or the capability of delighting the customer.
How companies collect and use big data was the topic of a presentation at the 16th annual Automotive Logistics Global Conference held in September in Detroit, which explored the impact and implications of big data across automotive logistics.
Many companies spend time and effort collecting data and trying to understand it. Even those that do it well often don’t know how to translate information into actions. Between diagnostic, prescriptive, predictive, and historic analytics, the end-game should be the same: creating an actionable data element.
How can companies make data actionable to help drive decisions and actions? When discussing data, the goal should be to simplify and compress the data and do the analysis to find the actionable component of the data set.
Consider these four key steps in the process:
- Collect data. Essential to making data usable is having databases in place to collect, review, and analyze what’s coming in. Data collection into a database must be seamless and almost automated to the point that you’re able to receive that data effortlessly in order to move it into the next steps.
- Visualize and analyze. Using interactive data visualization tools focused on business intelligence, various data points and elements effectively jump out as opportunities. Deeper than any spreadsheet, users can create visualizations of density, prior locations, inbound supplier networks, even density of freight by geography or other variables.
- Understand and optimize the data. Using a supply chain design optimizer and/or simulator, for example, organizations can work at strategic, tactical, and operational levels to run optimization models and further understand and capitalize on data.
- Put information into action. Data is only valuable if it’s actionable. One Tier 1 supplier took the previous three steps to reconstruct the current inventory model to support manufacturing. The supplier was able to lower inventory carry costs to have more of the right material based on geographic location and distance traveled.
For manufacturing logistics operations, data provides incredible inventory optimization opportunities related to use of containers and LTL freight. By linking production schedules, organizations can ensure better alignment of materials on hand and the supportive transportation mode used. For service parts and finished vehicle logistics, actionable data can be supported through direct shipment optimization, and improved cross docking closer do individual dealers, which is more advantageous than going through a national or primary distribution centers.
The process can also help optimize container use. Whether streamlining or standardizing container types across suppliers and plants, tapping external pooling, or improving truckload utilization, organizations can save money by building better loads. By using big data to improve utilization, organizations can reduce truckload conveyances – and realize real savings.
Ultimately, maximizing data opportunities is only useful if the right actions are taken, often with the assistance of a 3PL partner versed in big data management. Ideally, actionable data should lower the cost of materials sourcing, manufacturing, logistics and supply chain management. If the action taken lowers costs, then the process was valuable indeed.
Authored by Tom Kroswek
Tom Kroswek is Senior Director of Supply Chain Excellence, Value Stream Leader – Automotive, Ryder Supply Chain Solutions. He is a logistics and engineering professional with 28 years of experience in supply chain engineering, transportation analysis, and inbound manufacturing support.
