Real-Time Order Acceptance in Transportation Under Uncertainty Authors: Hiral Nisar and Josh Rosenzweig Sponsor/Advisor: Dr. Chris Caplice Advisor: Dr. Francisco Jauffred MIT SCM Research Fest May 22, 2014

MIT SCM ResearchFest May 22, 2014 Key Question Accept? Reject? We sought to create a model for effective order acceptance decisions in a transportation/distribution setting. Lot of industries that have to make decisions to accept, reject, or even prioritize orders incorporating constraints with future demand unknown. For example: MTO industry (accept, reject, and prioritize orders based on considerations such as line utilization, set-up times, warehouse space allotment, revenue, customer relationships), (orders can come in later that make a perfectly reasonable decision costly in the future). Specifically looking at distribution in the retail industry in the online domain, lots of growth recently. Can demand probabilities be used to make instantaneous order acceptance decisions that allow for effective capacity utilization? MIT SCM ResearchFest May 22, 2014

Retail Distribution Center Parcel Delivery Company Motivation External Internal Retail Distribution Center Parcel Delivery Company Private Fleet Amazon story. Story in the news last year about Amazon not meeting demand on a large scale. They relied fully on a parcel delivery company, UPS, and neither company was prepared for the unexpectedly huge spike in demand, partly due to certain amazon subscribers who are guaranteed 2 day delivery time. Big PR nightmare. Using a parcel delivery company takes control away from the retailer. Remove reliance from parcel delivery companies to retain control over their distribution system, however, private fleet capacity is not infinite, retail companies will make sure fleet size can cover most of demand, but not ALL demand. There will always be a need for third party logistics companies to some extent. The real question here is deciding which orders companies should take on with their private fleet, and we’ve created a model to facilitate effective decision making. Mitigate reliance with a portfolio approach. Customer MIT SCM ResearchFest May 22, 2014

MIT SCM ResearchFest May 22, 2014 Motivation Region B Region A To illustrate the underlying mechanics of our model, I’ll show you a simplified diagram of how the constraints effect transportation. Two situations, we have the ability to serve six homes. On the left we accepted orders for two low density regions, on the right we accepted orders for one high density region. We may actually be losing money by delivering to region C. Quite obviously we like the situation on the right. Region C MIT SCM ResearchFest May 22, 2014

MIT SCM ResearchFest May 22, 2014 Model Objective Use probabilistic demand data to make effective real-time order acceptance/rejection decisions by: Prevent underutilized capacity Eliminating unprofitable truck tours Main driver of losses is committing to truck shipments in which our revenues won’t exceed our costs. MIT SCM ResearchFest May 22, 2014

MIT SCM ResearchFest May 22, 2014 Model Structure Probability of receiving breakeven number of totes within the time left in the acceptance period MIT SCM ResearchFest May 22, 2014

Model Criteria: Regional Breakeven This is an example for a region. Explain how cost is calculated. B MIT SCM ResearchFest May 22, 2014

Model Criteria: Likelihood of Receiving Totes Compare number of totes to receive in time left by the breakeven number of totes. Explain 3 three levels of certainty. MIT SCM ResearchFest May 22, 2014

MIT SCM ResearchFest May 22, 2014 Testing Strategies Myopic Manager Model Accept all orders sequentially Instantaneous Logistic Regression Model Accept orders based on binary function output Distance and order size as explanatory variables Optimal Model (Baseline) Accept orders that maximize capacity and profits At the end of the order acceptance period MIT SCM ResearchFest May 22, 2014

MIT SCM ResearchFest May 22, 2014 Data 19 regions 4779 customers Each customer orders once a month 20 days simulated daily demand data Greater Boston Region MIT SCM ResearchFest May 22, 2014

MIT SCM ResearchFest May 22, 2014 Model Assumptions Order acceptance period of 12 hours New truck is started when it is possible to receive breakeven number of orders with 70% certainty Each truck has a fixed capacity of 80 totes Fixed cost per truck is $80 Each order has multiple totes and revenue per tote is $40 MIT SCM ResearchFest May 22, 2014

MIT SCM ResearchFest May 22, 2014 Results MIT SCM ResearchFest May 22, 2014

MIT SCM ResearchFest May 22, 2014 Results MIT SCM ResearchFest May 22, 2014

Results: 13 Truck Fleet Size Model Type Average Daily Profit ($) Difference from Optimal ($) % Difference from Optimal Optimal 2894 - Myopic Manager 1792 (1102) (38.1) Probabilistic 2691 (203) (7.0) Logistic Regression 1652 (1242) (42.9) MIT SCM ResearchFest May 22, 2014

MIT SCM ResearchFest May 22, 2014 Conclusion Demand probability distribution is an important component for use in real-time operational decision making processes Logistic regression analysis is not appropriate for an order acceptance model MIT SCM ResearchFest May 22, 2014

MIT SCM ResearchFest May 22, 2014 Future Research Real demand data Shared truck capacity between regions Penalties for rejecting the order Comparison with actual strategies used by companies MIT SCM ResearchFest May 22, 2014

MIT SCM ResearchFest May 22, 2014 Questions? MIT SCM ResearchFest May 22, 2014