Estimating and benchmarking Less-than-Truckload market rates

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<ul><li><p>requipanily Chcons1.01ding ortantchain</p><p>LTL mode differentiates itself from the other modes, because the shippers do not pay for the entire truck/container costbased on rate permile, but they pay only a portion based on their own freight. The LTL carriers are therefore interchangeably</p><p>1366-5545/$ - see front matter 2010 Elsevier Ltd. All rights reserved.</p><p>* Corresponding author. Tel.: +1 404 457 2950; fax: +1 404 335 3812.E-mail addresses: (E. zkaya), (P. Keskinocak), (V. Roshan Joseph), weightr@schneider.</p><p>com (R. Weight).1 Tel.: +1 404 894 2325.2 Tel.: +1 404 894 2301.3 Tel.: +1 920 592 6770.</p><p>Transportation Research Part E 46 (2010) 667682</p><p>Contents lists available at ScienceDirect</p><p>Transportation Research Part E</p><p>journal homepage: www.elsevier .com/locate / t redoi:10.1016/j.tre.2009.09.004delivery of smaller shipments at lower cost. Less-than-Truckload (LTL) is a mode of transportation that serves this need byhandling shipments smaller than full-truckload and larger than small package. LTL is a $34 billion industry in the US and LTLfreight is priced signicantly higher per unit weight than truckload (TL) freight (Schulz, 2007). Given the trade-off betweenhigher service levels and higher cost of LTL shipments, LTL purchasing managers increasingly focus on getting better ratesfrom the carriers. However, often times neither a customer nor an LTL carrier knows how the offered rates compare tothe other rates for similar shipments in the industry. Since every shippercarrier pair contract their own rates based on manyparameters, the knowledge of market rates requires historical shipment data from a variety of shippers and carriers and asystematic process for analyzing the data.1. Introduction</p><p>Todays competitive marketplacemarket knowledge and the price comof Logistics report by Council of Suppin 2007, up 7% from 2006 costs, and2003 when the logistics costs were $are trying to gain a better understanvices. Negotiations become an impo</p><p>With the globalization of supplyres companies to operate at low-cost, which increases both the importance of thees are willing to pay for acquiring such knowledge. According to 19th Annual Stateain Management Professionals, the logistics costs add up to $1.4 trillion in the UStitute 10.1% of the US GDP of the same year, following an increasing trend sincetrillion with 8.6% of the US GDP (CSCMP, 2007). To reduce logistics cost, shippersf the market rates offered by the carriers or other logistics providers for their ser-part of cost savings in this Business-to-Business (B2B) market environment.s in the manufacturing and retail industries, there is an increasing need for fasterEstimating and benchmarking Less-than-Truckload market rates</p><p>Evren zkaya a,*, Pnar Keskinocak a,1, V. Roshan Joseph a,2, Ryan Weight b,3</p><p>aH. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, 765 Ferst Drive, Atlanta, GA 30332, United Statesb Supply Chain Advisory Services, Schneider Logistics Inc., 3101 S. Packerland Dr., Green Bay, WI 54313, United States</p><p>a r t i c l e i n f o</p><p>Article history:Received 18 July 2008Received in revised form 17 May 2009Accepted 9 June 2009</p><p>Keywords:Less-than-TruckloadMarket rate modelsPricing structureBenchmarking</p><p>a b s t r a c t</p><p>In this paper, we present a regression-based methodology that can estimate the Less-than-Truckload (LTL) market rates with high reliability using an extensive database of historicalshipments from continental United States. Our model successfully combines the quantita-tive data with qualitative market knowledge to produce better LTL market rate estimateswhich can be used in benchmarking studies allowing carriers and shippers to identify costsaving opportunities. We identify the main drivers of LTL pricing and reveal the effects ofcertain industry practices on the nal market rates.</p><p> 2010 Elsevier Ltd. All rights reserved.</p></li><li><p>as an additional surcharge on top of the LTL rate. Fuel surcharge is stated to be a major problem across the industry, which</p><p>668 E. zkaya et al. / Transportation Research Part E 46 (2010) 667682originally emerged to protect carriers from sudden increases of fuel costs. However, the LTL industry lacks a standard fuelsurcharge program. FedEx CEO Douglas Duncan states that there is inconsistency in pricing in the LTL market since dereg-ulation in 1980. Every customer has a different idea of pricing in their base rates and surcharges (Hannon, 2006b). Grant andKent (2006) survey the methods used by LTL carriers to calculate fuel surcharges. Extra services provided by LTL carriers suchas pallet handling are also charged separately under accessorial charges. Barrett (2007) explains the evolution of free marketinto a very complex pricing structure: Things soon got out of control in the newly invigorated competitive marketplace.Carriers bulked up their base rates outrageously, to support more and more increases in those customer-attracting discounts,and the process became self-perpetuating. Thus it is that discounts in the 70th, even the 80th percentile have become theorder of the day now. Recently, there are further attempts to remove remaining regulations on the LTL industry. SurfaceTransportation Board (STB) (formerly, Interstate Commerce Commission) decided on May 7, 2007 (Ex Parte No. 656) to re-move anti-trust immunity previously enjoyed by LTL carriers who met at the National Motor Freight Committee (NMFC)meetings to set classication of goods or at rating bureaus. The impact could potentially eliminate the NMFC or freight clas-sication of commodities. Future studies could replace freight class with freight density, which are highly correlated. How-ever, there is no nal decision on this major change yet (Bohman, 2007).</p><p>While there has been little research done to analyze industry practices in pricing the LTL services, researchers investi-gated other interesting aspects of the LTL industry. On the operational side, Barnhart and Kim (1995) analyze routing modelsfor regional LTL carriers. Chu (2005) develops a heuristic algorithm to optimize the decision of mode selection betweentruckload and LTL in a cost effective manner. Katayama and Yurimoto (2002) present a solution algorithm and correspondingliterature review for LTL load planning problem for reducing LTL carrier operating costs. Hall and Zhong (2002) investigatethe equipment management policies of the long-haul LTL shipments. Murphy and Corsi (1989) model sales force turnoveramong LTL carriers. Chiang and Roberts (1980) build an empirical model to predict transit time and reliability of the LTLshipments. An operations model constructed by Keaton (1993) analyzes and reports signicant cost saving opportunitiesin economies of trafc density. Many mergers and acquisitions in the LTL industry can be explained by this potential costsavings opportunity. Considerably large literature is devoted to the economics of the trucking industry. Smith et al.(2007) highlight the studies that examine the cost structure of motor carriers with particular emphasis on the effect ofthe deregulation. Winston et al. (1990) carefully investigate the history of surface freight deregulation and sheds light oncalled common carriers in the transportation industry. In LTL, since shipments belonging to different shippers are carried inone truck, the pricing structure is much more complex compared to truckload shipments. For carriers, it is a very challengingtask to estimate what the real costs are for different loads. LTL carriers use a transportation network with break-bulk facilitiesand consolidate LTL freight to a full-truckload or break a full-truckload into local deliveries. These facilities incur extensivehandling and planning costs, which are hard to track down to ration to each shipper. To simplify the pricing structure, thecarriers use industry standards called tariffs. Based on these tariffs (such as Yellow500 and Czarlite) the freight is pricedbased on its origindestination (OD) zip codes, its freight class and its weight (15012,000 lbs). However, these tariffs areoften used as a starting point for negotiations and the carriers usually offer steep discounts (5075%) from the tariffs.</p><p>The main goal of this study is to develop an analytical decision-support tool to estimate LTL market rates. To the best ofour knowledge, such a tool currently does not exist in the industry. Having market rate estimates which consider variousfactors such as geographic area, freight characteristics and relative market power of the shipper (or carrier) will help ship-pers better understand how much they currently pay with respect to the market and why, and whether there are opportu-nities for cost savings. Shippers can also use these estimates in their network design studies as a source of reliable LTL pricesfor the proposed new lanes. On the other hand, carriers would benet from market rate estimates in pricing their services. Asimilar-purpose analytical benchmarking model, Chainalytics Model-Based Benchmarking (MBB), has been developed byChainalytics, a transportation consulting rm, for the long-haul truckload and intermodal moves. MBB analyzes the costdrivers for the realized market rates of shippers that form a consortium and share shipment data. MBB only shares infor-mation regarding the drivers of transportation costs not the actual rates themselves. . . . [It] quanties the cost impact ofoperational characteristics (Chainalytics, 2008). In our analytical model we not only quantify the impact of tangible factorsthat are captured with the data, but also analyze the market information that is not currently captured with any data col-lection procedure and provide the full view of the cost drivers of LTL market rates. Our results show that qualitative expertinputs on shipper and freight characteristics, such as the desirability of the shippers freight perceived by the carriers, ship-pers negotiation power and the value shipper gets from the service, can be crucial in LTL pricing. This information can thenbe quantied and used in the econometric models to further improve the market rate estimations. Suggested methodologycan also be applied to other B2B markets for improving price estimations with qualitative market information.</p><p>2. Literature review</p><p>In the LTL business, rates continue to rise and with the sharp increase in fuel charges in recent years, LTL purchasing man-agers are looking for different ways to reduce cost (Hannon, 2006a), e.g., using standardized base rates, always asking fordiscounts, and using online bidding tools (Hannon, 2006b). The deregulation of the LTL industry with the Motor CarrierAct of 1980 brought todays complex pricing structure. Leaving carriers free for setting any discount levels, shipment ratesstarted to be called with their percent discounts off of the carrier set base prices. Later, fuel charges sky rocketed when added</p></li><li><p>E. zkaya et al. / Transportation Research Part E 46 (2010) 667682 669We use a dataset of shipments from February to April 2005 containing information about origin and destination zip codes,cities and states, the carrier and shipper names, the weight, the class of the freight, the total line haul price paid to the carrier,the unique bill of lading number, and some other operational information such as date of shipment.</p><p>The cleaned dataset contains $90 million worth of LTL transactions incurred by 485 thousand shipments during these3 months, spanning the freight of 43 shippers moved by 128 carriers that covers 2126 state-to-state lanes (92% of all possiblestate-to-state combinations inside the continental US excluding Washington DC). While these LTL transactions come fromfreight payment data that was electronically audited and compared against shippercarrier contracts, some processing er-rors may still occur. Therefore, cleaning procedure involves removing shipments that are missing or have erroneous crucialinformation such as zip code, freight class, and weight. Also, the dataset is ltered to include shipments that are within rea-sonable LTL market bounds such as the weight to be between 100 and 12,000 lbs (higher than 12,000 lbs is generally morecost effective with truckload shipments) and minimum discount is set to 40%. The discount levels range from 50% to 75% forthe majority of the shipments. With regards to lane coverage, diversity of transactions among freight class and diversity ofcarriershipper combinations, we used one of the most extensive dataset available in the industry.</p><p>3.2. Seasonality</p><p>LTL prices are negotiated for long term (i.e., 1 or 2 years) and contracted. Price contracts cover the entire contract periodwith the same negotiated prices without including possible seasonal changes of the logistics costs. Therefore, the seasonalityis generally not part of the LTL pricing (excluding the fuel surcharges that are seasonally affected by constantly changing fuelthe economic effects on the LTL industry. Among the researchers focusing on the carrier side of the equation, Spady andFriedlaender (1978) propose hedonic cost functions to model the cost structure of the trucking industry including qualityof service as one of the cost drivers. Hall (1999) explores the cost of freight imbalances by analyzing a major LTL carriersUS network operations. Halls conclusions suggest that ow imbalances can be a signicant source of added transportationcost for certain lanes. On the shipper side, researchers propose different ways of reducing the transportation cost for theshipper. Elmaghraby and Keskinocak (2003) analyze combinatorial auctions focusing on an application by The Home Depotin transportation procurement and report signicant cost savings. Carter et al. (1995) investigate the LTL weight discountpractices to uncover potential savings for shippers. None of these studies, however, use shipment level actual data, whichis required to understand the main drivers of LTL market prices. Smith et al. (2007) analyze a US LTL carriers shipment datawith statistical models to estimate revenues from different customers at different lanes. They compare the regression esti-mated expected revenues with actual revenues to identify opportunities for re-negotiations when there is a systematic dif-ference in estimated and actual revenues. Their models do not estimate market rates at individual shipment level, and theiranalysis is limited to a single carriers dataset. Effective market rate estimation requires a diverse set of carriers and shipperswith diverse freight characteristics. Although many articles such as Centa (2007) and Baker (1991) reveal that there aremany factors considered in the LTL pricing and it is a complex mechanism of convoluted relationships and considerations,we nd no study so far that attempts to analytically model the LTL pricing structure and estimate individual LTL shipmentmarket rates. With our research, we aim to ll this gap by analyzing LTL industry data with statistical methods to...</p></li></ul>