Carrier Strategies in the Spot Trucking Market Authors: Paul Jeffrey R. Leopando Kyle A.C. Rocca Advisor: Dr. Chris Caplice Sponsor: Coyote Logistics MIT SCM ResearchFest May 22, 2014

Given a set of available loads, which, if any, should a carrier take? Problem Statement Given a set of available loads, which, if any, should a carrier take? Omaha, NE Chicago, IL 1 2 3 Milwaukee, WI Minneapolis, MN Loaded Empty Set up problem statement Explain deadheading, repositioning Mention decision with uncertainty of P(Onward Load) SIMPLIFIED IMAGE: more than 3 loads, multitude of loads at each destination Decision parameters Load 1 Load 2 Load 3 Total revenue $700 $1000 $850 Revenue per mile (RPM) $1.84 $1.95 $1.80 Onward load likely? Yes No N/A 2

Key Findings Can disaggregate load decision Complex strategies > simple strategies Deadhead distance = most important factor Distinct results by geographic location 3

Overview Problem Statement Key Findings Research Context Project Goals Research Design Data Profiling Initial Simulation Testing Strategy Testing 4

Research Context Spot Market Owner-Operators No more than 5 vehicles 30,000 carriers Operating strategy: Maximize utilization Take long hauls Project proposal from a freight brokerage company, tasked with matching carrier capacity and shipper freight in the spot market. Spot market loads are negotiated as one-offs Owner operators are the largest class of carrier by number of businesses servicing spot 30,000 carriers could theoretically have 30,000 different strategies We know some things about their strategies, but it hasn’t been widely studied 5

Project Goals Carriers Understand how load selections affect success 5/8/2018 Project Goals Carriers Understand how load selections affect success Brokers Improve matching of capacity and freight Explain success metrics – revenues earned and utilization Key up data slide All well and good, but where do we begin to answer our research question? Masked shipment data from a proprietary load board, wealth of information, but difficult to divine strategies. Scholars Fill the gap in transportation literature 6

LoadID 2312209 2315311 2311913 2312708 LoadDate 6/1/13 Equipment V,R V Miles 20 66 78 1230 RPM 19.25 9.09 5.13 1.71 OrigCity Stockton Atlanta Downey Paw Paw OrigState CA GA MI DestCity Tracy Duluth Oceanside Arcadia DestState FL TotalCost 385 600 400 2,100 LoadBuild 5/31/13 7:46 5/31/13 14:29 5/30/13 19:51 5/31/13 9:16 LoadBooked 5/31/13 9:48 6/1/13 8:48 5/31/13 12:23 5/31/13 11:21 TruckArrival 6/1/13 2:30 6/1/13 13:05 6/1/13 8:30 6/1/13 16:27 PickupAppt 6/1/13 4:00 6/1/13 15:00 6/1/13 9:00 6/1/13 13:00 TruckDelivery 6/1/13 9:45 6/1/13 17:00 6/1/13 13:45 6/3/13 2:56 CarrierID 26126 125042 176944 55981 CarrierHQCity Mableton Fontana Harrison CarrierHQState AR CarrierUnits 13 1 9 Project Goals: “This is what we want to achieve” [data]: “But this is what we have.” [Research Design]: so this is how we thought about attacking the question 7

Research Design Data Profiling Initial Testing Strategy Testing Simulations 8

Data Profiling Masked shipment data Scrubbed data: Objectives: 5/8/2018 Data Profiling Masked shipment data May to mid-October, 2013 Scrubbed data: 207,592 loads 13,117 carriers Objectives: Summarize data Define relevant features of loads First thing – what is the data? What features does it have? Includes fields for: Carrier ID Equipment Origin and Destination coordinates Time stamps: Entered into system Assigned a carrier Scheduled pick-up Actual pick-up Second – what analysis can we do ----- Meeting Notes (5/14/14 13:58) ----- more here, characteristics 9

Data Profiling Objective: Summarize data Total Owner-Operators Generally aligned with industry trends Total Owner-Operators Number of Carriers 13,117 7,590 (58%) Avg. Length of Haul (miles) 619 593 Avg. Revenue per Loaded Mile ($/mi) 2.33 2.51 Discuss revenue per loaded mile 10

Utility Function for Scoring Attractiveness Data Profiling Objective: Define relevant features of loads Utility Function for Scoring Attractiveness α, β, γ, δ: Relative weights Score = ( α [Length of Haul] + β [RPM] Defined relevant features of loads and created utility function to score loads Can use in a simulation Can apply a linear additive function 3 features of loads, one of carrier ----- Meeting Notes (5/14/14 13:58) ----- change alignment + γ [P(Onward Load)] + δ [Deadhead Distance] ) 11

Boston, MA Chicago, IL Los Angeles, CA Atlanta, GA Dallas, TX Start Review Test Load Discard Test Load Validate Test Load Feasibility Start All Loads During Wait Time Evaluated? Feasible? No animate

Initial Simulation Testing Objective: Define relevant operating procedure parameters Model parameter Initial case Alternative cases Haul length preference Long Short Waiting time (hours) 1 2, 3, 5, 7, 9 Speed (mph) 55 40, 45, 50 Deadhead ceiling, c (%) 10 15, 17, 20, 25 α: Length of Haul 0.25 0, 0.3, 0.5, 1 β: RPM 0, 0.3, 1 γ: P(Onward Load) δ: Deadhead distance Model parameter Initial case Alternative cases Haul length preference Long Waiting time (hours) 1 Speed (mph) 55 Deadhead ceiling, c (%) 10 α: Length of Haul 0.25 β: RPM γ: P(Onward Load) δ: Deadhead distance Deadhead ceiling vs. deadhead weight relevant: length of haul, truck speed Fix variables: long, 2, 55, 20. 13

Fixed parameters: Long-haul, 2 hr wait, 55 mph, 20% deadhead Strategy Testing Strategies Simple Complex L R P D LD LRD LPD LRPD α: Length of Haul 1 0.5 0.3 0.25 β: RPM γ: P(Onward Load) δ: Deadhead distance Fixed parameters: Long-haul, 2 hr wait, 55 mph, 20% deadhead arbitrary 14

Strategy Testing Results Simple Complex L R P D LD LRD LPD LRPD Total Revenue ($) Atlanta 87,692 83,402 77,615 139,709 181,450 178,043 168,220 165,460 Boston 176,819 88,132 94,132 146,726 168,525 182,363 168,255 179,913 Chicago 79,329 84,034 84,944 151,960 186,729 198,256 162,535 184,545 Dallas 125,100 97,611 85,907 151,002 172,672 186,108 167,083 162,693 Los Angeles 86,148 81,264 80,887 155,093 175,649 191,923 172,018 189,170 Utilization (% loaded) 51 48 47 87 95 91 93 90 77 27 92 89 50 46 64 38 88 94 54 35 52 86 Quickly cover main points: -Complex strategies superior to simple -Deadhead single most important factor ***relative to each other ----- Meeting Notes (5/14/14 13:58) ----- explain cities decision with uncertainty center use an example case callouts % of best (months) ----- Meeting Notes (5/14/14 14:01) ----- center numbers 39% of maximum 15

Strategy Testing Results Home city: Chicago, IL R LRD Strategy LRD Strategy R Strategies R LRD Total revenue ($) 16,050 42,644 Utilization (% loaded) 50 90 Total distance (mi) 10,045 24,704 Number of loads 25 16 Loaded Empty Month: September 16

Strategy Testing Results Strategy LRD All Owner-Operators Avg. length of haul (mi) 1,317 593 Avg. RPM ($/mi) 1.89 2.51 Long-Haul Owner-Operators 1,280 1.63 > Strategy LRD produces 16% improvement in average RPM. 17

Key Findings Can disaggregate load decision Complex strategies > simple strategies Deadhead distance = most important factor Distinct results by geographic location Less text ----- Meeting Notes (5/14/14 13:58) ----- data mining - ALOH, etc. problem statement: explain revenues and utilization explain all 5 cities 18

Future Directions Expand scope of data and inputs: Different carrier types, equipment, data Longer season Improve simulation model: Hours of Service (HOS) restrictions Personalize: Profit function Return-home pressure Excluded states 19

Acknowledgments Partners at Coyote Logistics: Advisors at MIT: Bill Driegert David Ossim Amit Prasad Ella Revzin Advisors at MIT: Chris Caplice Lenore Myka Thea Singer 20

Questions? 21