Milk Run Inbound Logistics Optimization for Toyota
Automating planning for Toyota.
Considered a global reference in supply-chain innovation with manufacturing operations in Thailand, the second-largest outside the US. Toyota export cars to other Asian countries and to the Middle East.
DecisionBrain was tasked with first helping automate the process of planning the transportation of auto parts from external suppliers to the customer’s plants, and then developing optimization models to minimize overall transportation cost while ensuring level of service.
A problem of too many manual planning hours, multiple plan revisions and cost inefficiency.
Before DecisionBrain project, a team of planners would manually define the monthly transportation plans of the auto part suppliers to the factories. The plans would be reviewed and approved by managers and then communicated to the operations teams that could also require some final adjustments. Manually planning required many of the planners’ hours that could be used for higher-level tasks. It took each planner 2.5 days to build a plan before it could be shared with management and operations. The validation process could also require several interactions.
All these man-hours dedicated to planning represented a significant operational cost for the company. Due to the very high volume of vehicles produced, the current inbound logistics planning process, mainly performed manually, was very complex and human-intensive. By applying optimization techniques the customer was expecting to significantly reduce the planning efforts and achieve a 2% reduction in total transportation costs.
A project focused on automating decisions and improving efficiency.
The first part of the project focused on automating the plans generation by 1) replicating with algorithm the planners decision making process and 2) embedding validation steps into the process. The objective of this phase was to generate similar plans in a much reduced time. This first phase was successfully delivered, reducing planning time from 2.5 days to 1 hour. The second phase of the project aimed at leveraging optimization techniques to produce more efficient plans that reduce the overall transportation costs while keeping the level of service.
The planning engine focuses on three specific areas:
- Orders Grouping: Which orders can go on each truck
- Trucks Routing: Optimizing the delivery routes
- 3D packing: Matching the package size to the truck volume, specific to the exact placement, and considering multiple different truck sizes and limitations
The solution is delivering a 10% reduction in total transportation costs, sizably above initial expectations of 2% reduction. Additionally the system is providing a plan in 1 hour compared to the original 2.5 days. The next phase will be a global roll out.
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