Key Notes
- Grouping Deliveries
- Everyday some set of deliveries to a subset
- Delivery constraints - Size of truck, time sla
- 200 Customers, 10 Trucks - different combinations
- Academic point of view not possible
- Never ever be able to go through all possibilities
- Route Optimizer - Combine computer possibilities with ideas
- Hard problem in delivery
- Changing traffic patterns
- Decreased parking availability
- Smaller deliverables
- Increasing cost per delivery
- Challenges meeting same day delivery
Challenges
- Too much emphasis on pieces
- Extensive manual effort to generate route plans
- Map on wall vs Map on computer vs real-time conditions
- Assuming no variability and uncertainty
- Continuous improvement is needed
- Mobile Device = Data Capture Device
- Data Science - Be smart dealing with data
- Not easy thing to figure out data is right
- With cloud store large data of customers
- Spot problems/inefficiencies
- Datahub is easy with tech
- Lat / Long in the ballpark
- Different Delivery problems
- Ecommerce has created a headache for the delivery business
- Handling off-cycle orders
- Planning for truck drivers
- Spread out
- Keep track of master routes
- Returns are expensive
- Master Routes
- Day Balance
- Deliver similar items on same truck
- Assign delivery day for efficient routes
- Data requirements
- Product data
- Volume estimations
- Truck data
- Stop time = fixed time + lineitems*variabletime
- Every customer has a distribution time
- Drivetime
- Google, bing, open street
- With lots of customers route optimization difficult
- Easier to build mobile platforms
Collect drop point samples
Keep Learning!!!
No comments:
Post a Comment