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Analyzing private communities on Internet-based collaborative transportation networks
Transportation Research Part E 43 (2007) 21–38 www.elsevier.com/locate/tre
Analyzing private communities on Internet-based collaborative transportation networks Rahul Kale
a,* ,
Philip T. Evers b, Martin E. Dresner
b
a
b
Coggin College of Business, University of North Florida, Jacksonville, FL 32224, United States Robert H. Smith School of Business, University of Maryland, College Park, MD 20742, United States Received 22 March 2005; received in revised form 21 June 2005; accepted 11 July 2005
Abstract The establishment of private communities on Internet-based transportation networks is a relatively new trend that has met with mixed success. Within industry, there has been uncertainty over the costs and benefits of these communities to shippers and carriers. Through a theoretical model based on assumptions derived from industry executives, this paper suggests that shippers may indeed benefit by establishing private communities. Further, the results show that in high-trust relationships carriers may be no worse off by cooperating with shippers in their private communities. Ó 2005 Elsevier Ltd. All rights reserved. Keywords: Collaborative transportation networks; Internet business models; Transportation management; Shipper– carrier relationships
1. Introduction The Internet offers a wide range of opportunities for conducting business. Some Internet companies operate as extensions of ‘‘brick and mortar’’ firms (for example, barnesandnoble.com) while others represent types of businesses that would not have been possible prior to the advent *
Corresponding author. Tel.: +1 904 620 1107. E-mail address: [email protected] (R. Kale).
1366-5545/$ - see front matter Ó 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.tre.2005.07.004
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R. Kale et al. / Transportation Research Part E 43 (2007) 21–38
of the Internet (for example, on-line database services such as Lexis-Nexus that deliver electronic text directly to end users). One new business category that has been facilitated by the Internet is electronic marketplaces. Although the best known of these marketplaces, ebay.com, caters mainly to consumers, other electronic marketplaces operate primarily in the business-to-business setting. An electronic marketplace is defined as an inter-organizational information system where buyers and sellers can meet to conduct business. Thus, a marketplace is a form of intermediary that establishes electronic links between buyers and sellers interested in conducting transactions (Choudhury et al., 1998). Internet marketplaces in existence include Covisint in the automobile industry, Travelocity in the airline industry, and The Seam in the cotton industry. Several electronic markets operate in the logistics field, bringing together buyers and sellers in such areas as transportation, warehousing, and manufacturing (e.g., Descartes Systems Group, Manhattan Associates, National Transportation Exchange, Nistevo, Transplace). The purpose of this paper is to model a transportation exchange and demonstrate its potential benefits and costs. In particular, the costs and benefits of ‘‘collaborative communities’’ to shippers and carriers are analytically explored. Collaborative communities are formed within transportation exchanges and facilitate the sharing of information between shippers and/or carriers.
2. Transportation exchanges Transportation exchanges are Internet services that bring together buyers (shippers) and sellers (carriers) of transportation services in order to increase the efficiency of both shipper and carrier operations. One way these exchanges benefit shippers is that they allow a larger number of carriers to bid for shipments, thereby increasing supply competition and reducing prices. Carriers, too, may benefit from transportation exchanges by gaining access to a large pool of shippers, allowing for increased capacity utilization and reducing ‘‘dead-hauls’’. Anecdotal accounts suggest that by 2001, there were as many as 100–250 transportation exchanges operating on the Internet (Pinkham, 2001). As with many other dot-com businesses, transportation exchanges have not been universally successful. Among the reasons most often cited for their failure are that shippers and carriers use the exchanges to transact only a small percentage of shipments and that shippers prefer to send loads via their trusted contract carriers rather than rely on Internet-facilitated spot market exchanges (Pinkham, 2001; Cooke, 2001). As well, carriers are reluctant to participate in transportation exchanges in order to avoid reducing their businesses to ‘‘commodity’’ status, thereby eroding profit margins. To increase their viability, some transportation exchanges have focused on facilitating existing relationships between shippers and carriers, using the market to supplement, rather than replace, existing relationships. A number of success stories have been reported in the trade press (c.f., Hannon, 2003). For example, Transplace.com, a technology-oriented third party logistics provider, manages the transportation for participating shippers by analyzing shipping lanes and identifying opportunities to combine loads across shippers. Transplace.com terms this concept ‘‘collaborative transportation management’’ (CTM). According to Esper and Williams (2003, p. 55), ‘‘CTM essentially involves converting order forecasts developed via CPFR [Collaborative Planning, Forecasting and Replenishment] into shipment forecasts, and insuring their accurate fulfillment’’.
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A term that is sometimes used to describe Internet-based collaborative services is ‘‘collaborative transportation networks’’. These networks (e.g., NTE.com or Nistevo.com) allow shippers and carriers to create ‘‘private communities’’ to better manage their transportation needs and resources. Depending on the rules of the community, shippers may share shipment information with their core carriers and/or participating shippers, in order to increase capacity utilization and reduce short shipments (c.f., Cooke, 2001; Cullen, 2001). Participating shippers can also identify backhaul opportunities for their contract carriers and, in turn, get a price break from the carriers (Strozniak, 2003). During discussions with industry officials regarding collaborative transportation networks, it was noted that no general rules govern all private communities. In certain instances, the owner of the community sets the rules, whereas in others cases the rules are set through open discussions between the participating shippers and/or carriers. However, based on these same discussions, it was concluded that collaborative transportation communities can generally be classified into three categories as shown in Fig. 1. The first is a ‘‘shippersÕ community’’. Typically, a shippersÕ community is geared towards improving the transportation performance of shippers. Shippers may share information on shipping requirements. If one shipper has extra needs, it can negotiate with a second shipper that has excess contracted capacity, thus creating cost savings for both shippers. The first shipper may receive below-market prices for carrier capacity, while the second shipper may avoid defaulting with its contract carrier for reneging on contracted capacity. With neutral exchanges, shippers and carriers may participate together in sharing information on shipping requirements and capacity availability (e.g., Cooke, 2001). Though these communities may be owned by shippers, neutral communities typically strive to benefit all of the participating parties. Therefore, carriers may achieve higher capacity utilization and shippers fewer short shipments through the sharing of information on neutral exchanges. Finally, it is conceivable that carriers could create carriersÕ communities to manage their relationships with shippers. Although no such arrangements (of which the authors are aware) currently exist, these communities would involve carriers sharing capacity and shipment information for their own benefit.
Shippers’ Community
Carriers’ Community
C1
Neutral Community
S2
C4
S4
S3
C5
S5
C3
S1 C2
S1 Shipper
C1 Carrier
Information flow
Fig. 1. General classification of on-line communities on transportation exchanges.
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3. Literature review and research questions Internet-based collaborative logistics networks are relatively new; consequently, very little academic literature deals directly with such networks. In the only relevant research paper found on the topic, Golicic and Davis (2003) presented a case study based on one such network. Though all of the participating shippers and carriers agreed that there were benefits to participating in these networks, none were able to definitively establish whether they were, on the whole, better off by doing so. Many aspects of collaborative communities are, however, similar to existing literature at the intersection of supply chain management and Internet operations. As a consequence, relevant research efforts from these fields and their implications for collaborative logistics communities are briefly noted below. A private collaborative community formed between a shipper and its carrier base is quite similar to an inter-organizational system (IOS). There have been several studies in the literature showing that information sharing between firms through an IOS results in improved performance. For instance, Internet-based collaboration between online retailers of music compact discs and their vendors may result in reduced inventory levels and cycle time for the same level of customer service. A consumer-direct-fulfillment arrangement between online retailers and their vendors may be facilitated through an IOS (Rabinovich, 2005). As Internet-based retailing facilitates the de-coupling of customer-order locations and inventory locations, online retailers may make better use of inventory location speculation and postponement strategies to improve inventory management (Bailey and Rabinovich, 2005). Electronic data interchange is one of the more common methods of establishing an information sharing relationship between two firms and has been shown to lead to reductions in inventory carrying costs, obsolete inventory, and emergency shipments (c.f., Mukhopadhyay et al., 1995). In addition, an IOS enables various supply chain initiatives such as Vendor Managed Inventory, Quick Response, Continuous Replenishment Planning, and Efficient Consumer Response. These programs have also been shown to result in reduced inventories, higher fill rates, shorter order cycles, reduced transaction costs, reduced transportation costs, and improved customer service (c.f., Waller et al., 1999; Lee et al., 1999; Daugherty et al., 1999). Though an IOS often leads to increased efficiencies, there are some issues that should be carefully considered by firms when evaluating their implementation. While information sharing between firms creates efficiencies, there is the potential for asymmetric benefits in which one firm receives a larger share of the gains than the other. Indeed, one firm may benefit at the expense of the other firm as adoption of an IOS could expose firms to the risks of opportunism or exploitation. A few conceptual papers allude to this possibility (e.g., Kumar et al., 1996; Premkumar, 2000). Given the potential for vastly varying benefits, participating parties may actually resist the implementation to enter IOS-based collaborative efforts (e.g., Clemons and Row, 1993). Examples of collaborative transportation networks creating win–win scenarios for shippers and carriers have been presented in the trade press. Given their potential to add value for shippers and carriers, it is interesting to ask why these networks are not commonly employed. It may be that firms are unaware of the benefits of participating in these networks. Or perhaps, firms are reluctant to share information with competitors, customers, and/or suppliers. In this paper, the conditions under which shippers and carriers are likely to be better (or worse) off by participating in a collaborative transportation network are investigated.
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Based on the above discussion, the following questions are considered: 1. As shippers (and carriers) form private collaborative communities on Internet-based collaborative networks, will such communities create efficiencies in shipper–carrier operations and, if so, by what means? 2. Is there a potential for asymmetric benefits, where one party may gain more than the other, depending upon the information sharing arrangement between them? 3. Is it possible that one party may benefit at the expense of the other? 4. Considering the above aspects, what are the conditions under which private communities are beneficial to shippers and to carriers? 4. Shipper performance model The models presented below are from a shipperÕs point of view. Similar implications arise for carriers. However, to avoid repetitive discussions, those implications will be merely stated where appropriate without presenting modeling details. 4.1. The modeling framework Consider a shipper–carrier dyad where the shipper has fairly stable transportation requirements between a pair of locations. One of the most common methods by which a shipper handles such requirements is to enter into a contract with a carrier. Both the shipper and carrier commit to a particular volume (q units, perhaps based on the average requirements of the shipper) and transportation rate (T per unit). Once the contract is set, day-to-day transactions ensue. A carrier sends a vehicle to the shipper with carrying capacity of q units. The shipper then tenders the shipment quantity of q units. However, occurrences are seldom deterministic. On a given day, either the shipperÕs requirements (S) or the carrierÕs availability (C) may deviate from q. If either C or S is greater than q, the excess may be handled via the spot transportation market. However, if C is less than q, then the shipper suffers in the form of either delayed shipments or lost sales. If S is less than q, then the shipper may end up either paying for unused capacity or paying for default penalties. With a conventional contractual relationship, the carrier becomes aware of the actual shipper requirements only when the carrier arrives at the shipperÕs dock. Similarly, the shipper becomes aware of the carrierÕs actual capacity only when the carrier arrives at the shipperÕs dock. By the time a party is aware of a contract default, it may be too late to take remedial actions. Therefore, the party suffers in terms of either delayed delivery, lost sales, or reduced capacity utilization. For this model, it is assumed that any shipments that cannot be made due to unavailable carrying capacity result in lost sales, while all shipments that do get shipped produce sales revenues (i.e., they are sold at a price of P per unit). It is further assumed that, if a shipper fails to provide q units, the shipper pays a penalty to the carrier for each unit of shipment defaulted (Ps per unit). Similarly, if a carrier defaults on its contracted capacity, it pays a penalty to the shipper for each unit of capacity it is short (Pc per unit). Any requirements or available capacity above the contract quantity are transacted through the spot market, composed of all other shippers and carriers, at a
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transportation rate of Ts per unit. It is assumed that the spot market is made up of a large number of shippers and carriers; thus, the spot market price is independent of the shipping requirements (or available capacity) of any individual shipper (or carrier). To summarize the notation: Contract quantity = q (units), Contract transport price = T ($/unit), Carrier penalty = Pc ($/unit), Shipper penalty = Ps ($/unit), ShipperÕs selling price = P ($/unit), Rs Shipper random requirement ¼ S ðunitsÞ with density f ðSÞ and c.d.f. F ðSÞ ¼ 0 f ðtÞ dt, Rc Carrier random availability ¼ C ðunitsÞ with density gðCÞ and c.d.f. GðCÞ ¼ 0 gðtÞ dt, and RT Spot market transport price ¼ T s ð$=unitÞ with density hðT s Þ and c.d.f. HðT s Þ ¼ 0 s hðtÞ dt. Shipper performance is measured by computing gross shipper profits as follows: Gross profit ¼ sales revenue transportation cost carrier penalty received=paid. 4.2. Conventional transportation management Given the modeling framework described above, there are four possible scenarios, shown in Fig. 2. In Case IA, both the shipper and the carrier default on the contract, however the shipperÕs
C
III
IV
q IB II IA q
S
Fig. 2. Possible shipper–carrier transaction scenarios. Note: The X-axis represents shipperÕs shipping requirements (S), while the Y-axis represents the carrierÕs capacity (C). q is the contract volume between the shipper and the carrier. The roman numerals enumerate all possibilities in a shipper–carrier transaction: IA: Both shipper and carrier default on the contract, however, S > C. IB: Both shipper and carrier default on the contract, however, S < C. II: Carrier defaults on the contract while shipper requirements are greater than the contract. III: Shipper defaults on the contract while carrier capacity is greater than the contract. IV: Both shipper and carrier honor the contract and have requirements/capacity greater than the contract.
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requirements are greater than the carrierÕs available capacity (a numerical example is given in Table 1). Thus, the shipper loses sales while the carrier pays a default penalty to the shipper for the capacity deficit. Referring to Table 1, the shipper loses sales of 20 units, while the carrier pays a penalty to the shipper for 20 units of lost sales (i.e., the difference between requirements and capacity). Using the gross profit expression, the shipperÕs profits for Case IA in a conventional system is as follows: Z s Z q ¼ fP c c T þ ðs cÞ P c gf ðsÞgðcÞds dc. ð1Þ S ProfitIA conventional c¼0
s¼0
The first two terms measure sales revenues and transportation cost, respectively, while the third term represents the penalty paid by the carrier to the shipper, as the carrier defaults on the contract in this case. In Case IB, both the shipper and the carrier default on the contract. However, in this case, the carrierÕs carrying capacity is greater than the shipperÕs shipping requirements. In the specific example shown in Table 1, the carrier incurs 20 units of unutilized capacity. The shipper compensates the carrier by paying a penalty for these 20 unused units. The following equation gives shipper profits in Case IB: Z q Z q IB fP s T s ðc sÞ P s gf ðsÞgðcÞ ds dc. ð2Þ S Profitconventional ¼ c¼s
s¼0
The first two terms again indicate the sales revenues and transportation cost, respectively, whereas the third term now gives the penalty paid by the shipper to the carrier, as the shipper defaults on the contract in this case. In Case II, the carrier defaults on the contract while the shipperÕs requirements are greater than the contract quantity. Since the shipper knows beforehand that its requirements are greater than the contract quantity, it arranges for spot transportation for the requirements above the contract quantity (in the example, 50 units). However, as the carrier defaults on the contract by 30 units, and since the shipper does not know in advance that the carrier is going to default, the shipper cannot use the spot market for the default quantity and loses sales equal to the carrier default. The carrier pays the shipper a penalty for this loss. The following equation measures shipper profits in Case II: Z q Z 1 ¼ fP c T c þ ðq cÞ P c S ProfitII conventional c¼0
þ
Z
s¼q P
fðs qÞ ðP T s Þghðts Þ dts gf ðsÞgðcÞ ds dc.
ð3Þ
T s ¼0
Table 1 Case examples for shipper–carrier transactions (contract quantity = 100 units) Case
Shipper requirements
Carrier capacity
IA IB II III IV
70 50 150 70 150
50 70 70 150 150
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The first three terms in Eq. (3) represent the revenues, transportation cost, and the default paid by the carrier to the shipper. The last, innermost integral gives the revenues earned by the shipper by placing shipments in excess of the contract quantity through the spot market. In Case III, the shipper defaults on the contract while the carrierÕs capacity is greater than the contracted amount. In this case, due to the shipper default, the carrier loses revenues equal to the shipment price for 30 units. The shipper pays a penalty to compensate for these 30 units of unutilized carrier space. Excess capacity above the contracted amount is sold on the spot market by the carrier (50 units). Shipper profits in Case III are as follows: Z 1Z q III fP s T s ðq sÞ P s gf ðsÞgðcÞ ds dc. ð4Þ S Profitconventional ¼ c¼q
s¼0
Finally, in Case IV, both the shipperÕs requirements and the carrierÕs capacity are greater than the contracted amount. Thus, both are able to honor the contract. The shipper sells the shipments above the contract quantity (50 units) through the spot market, while the carrier sells its excess capacity on the spot market (50 units). The following equation gives shipper profits for Case IV: Z 1 Z 1 Z P IV S Profitconventional ¼ P qT qþ fðs qÞ ðP T s Þghðts Þ dts f ðsÞgðcÞ ds dc. c¼q
s¼q
T s ¼0
ð5Þ
Profittotal conventional Þ
The total expected shipper profits ðS using conventional shipping methods under all cases is given by the sum of all the five expressions: IA IB II S Profittotal conventional ¼ S Profitconventional þ S Profitconventional þ S Profitconventional IV þ S ProfitIII conventional þ S Profitconventional .
The revenue and cost coefficients derived from Eqs. (1)–(5) are shown in Table 2, under the column heading ‘‘Conventional Transaction’’. It is assumed that a shipper will only ship products through the spot market if the spot market price for carrier capacity, Ts, is less than or equal to the profit made by selling the product, P. For the sake of convenience, this is shown in Table 2 simply by multiplying the units that the shipper plans to send through the spot market by the per unit expected revenues earned by the shipper on these excess units. The term E(Profit)spot indicates the expected per unit profit earned by the shipper by shipping excess units through the spot market. 4.3. ShippersÕ community Before examining performance on a shippersÕ community, it is necessary to characterize shipper–carrier relationships as either ‘‘low-trust’’ or ‘‘high-trust’’. A high-trust relationship is one where the shipper–carrier pair has a long standing contract and both parties are concerned about maintaining this relationship. A low-trust relationship is one where the shipper and carrier are focused only on their own short-term performance. As described below, the degree of trust in the relationship will affect default quantities and thus profit performance. By using a shippersÕ community and thereby obtaining information about carrier capacities prior to shipments being tendered, a shipper obtains two benefits. First, the shipper may be able to default its contract carrier without the possibility of recourse (shown in Case IA). In this case,
Case
Conventional system
ShippersÕ exchange
CarriersÕ exchange
Neutral exchange
IA
P * c T * c + (s c) * Pc
P * c T * c + (q c) * Pc + (s c) * (P Ts) * E(Profit)spot (LT) or P * c T * c + (s c) * Pc + (s c) * (P Ts) * E(Profit)spot (HT)
P * c T * c + (s c) * Pc
P * c T * c + (s c) * Pc + (s c) * (P Ts) * E(Profit)spot
IB
P * s T * s (c s) * Ps
P * s T * s (c s) * Ps
P * s T * s (q s) * Ps (LT) or P * s T * s (c s) * Ps (HT)
P * s T * s (c s) * Ps
II
P * c T * c + (q c) * Pc + (s q) * (P Ts) * E(Profit)spot
P * c T * c + (q c) * Pc + (s c) * (P Ts) * E(Profit)spot
P * c T * c + (q c) * Pc + (s q) *(P Ts) * E(Profit)spot
P*cT * c + (q c) * Pc + (s c) * (P Ts) * E(Profit)spot
III
P * s T * s (q s) * Ps
P * s T * s (q s) * Ps
P * s T * s (q s) * Ps
P*sT*s (q s) * Ps
IV
P * q T * q + (s q) * (P Ts) * E(Profit)spot
P * q T * q + (s q) * (P Ts) * E(Profit)spot
P * q T * q + (s q) * (P Ts) * E(Profit)spot
P*qT*q + (s q) * (P Ts) * E(Profit)spot
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Table 2 Coefficients for shipper and carrier profit functions
Note: (LT) = low-trust shipper–carrier relationship; (HT) = high-trust shipper–carrier relationship.
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both the shipper and the carrier default on the contract, but the shipperÕs requirements are greater than the carrierÕs capacity. In a ‘‘low-trust’’ relationship, a shipper may default a carrier against the contract quantity, instead of the actual shipment quantity tendered by the shipper (see Fig. 3). This is termed an ‘‘unfair’’ default, and in a high-trust relationship this default would not be charged. Using the example from Table 1, in Case IA, a shipper penalizes a carrier by 50 units (i.e., defaulting the carrier against the contract) instead of penalizing the carrier by the fair amount of 20 units (i.e., the actual lost sales for the shipper). A second benefit of shippersÕ communities to shippers is that with the advance knowledge of carrier capacity, a shipper may be able to make better use of the spot market. This may happen when the carrier defaults on its contract and the shipperÕs requirements are greater than the carrierÕs capacity (i.e., in Cases IA and II). In Case IA (Fig. 3), both the shipper and the carrier default on the contract, however the shipperÕs requirements are greater than the carrierÕs capacity. Under conventional contracting, a shipper would lose sales due to the carrier default (20 units, in Table 1 example). On a shippersÕ exchange, the shipper would have prior information on carrier capacity and would, therefore, know in advance that the carrier is going to default on the contract. With this advance knowledge, the shipper can plan to send a shipment equal to the carrierÕs default quantity (20 units) through the spot market, assuming the shipper earns positive profits (P > Ts). The profit equation for Case IA in a shippersÕ exchange can be stated as follows: Z s Z q S ProfitIA ¼ P c T c þ ðq cÞ P c shippers’ exchange c¼0 s¼0 Z P þ fðs cÞ ðP T s Þghðts Þ dts f ðsÞgðcÞ ds dc. ð6Þ T s ¼0
C
III
IV
q IB Carrier default on a shippers’ exchange
Carrier default in a conventional transaction
50 IA
II
70
q S
Fig. 3. Case IA: ShippersÕ exchange vs. conventional method. Note: Considering Case IA from Table 1, the shipperÕs requirements (S) are 70 units and the carrierÕs capacity (C) is 50 units. Thus, both default on the contract though S > C. In a conventional transaction, the carrier would be liable to the shipper for 20 units of lost sales. However, in a lowtrust relationship on a shippersÕ exchange, since the carrier does not know the actual shipper requirements, a shipper may default the carrier by 50 units instead of the fair default of 20 units.
R. Kale et al. / Transportation Research Part E 43 (2007) 21–38
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The last term (innermost integral) in Eq. (6) represents the revenues earned by the shipper from sending the default quantity through the spot market. In the case of a shippersÕ exchange, the coefficient for the carrierÕs default is (q c) instead of (s c) as it is under conventional contracting. The difference represents the unfair default (q c) (s c) = (q s) which a shipper may earn on a shippersÕ exchange in a low-trust relationship (equal to 30 units in the example given in Table 1). In a high-trust relationship, however, shipper profits on a shippersÕ exchange in Case IA are as follows: Z s Z q IA P c T c þ ðs cÞ P c S Profitshippers’ exchange ¼ c¼0 s¼0 Z P þ fðs cÞ ðP T s Þghðts Þ dts f ðsÞgðcÞ ds dc. ð7Þ T s ¼0
For a high-trust relationship, it is assumed that the default quantity is the fair quantity. The shipperÕs earnings from the spot market are the same as in Eq. (6), while the carrier penalty is the same as in the conventional method (Eq. (1)). In Case II (see Fig. 4), the shipperÕs requirements are above the contract quantity and the carrier defaults on the contract. The shipper may benefit from advance information about the carrierÕs capacity and send all of its goods in excess of carrier capacity through the spot market (80 units in the example), instead of just the quantity above the contract (50 units). The shipper profits in this case are as follows:
C
IV
III
q
Spot market sales on a shipper’s exchange IB
70 IA
Spot market sales in a conventional transaction q
Lost sales in a conventional transaction
150
II
S
Fig. 4. Case II: ShippersÕ exchange vs. conventional method. Note: Considering Case II from Table 1, the shipperÕs requirements (S) are 150 units and the carrierÕs capacity (C) is 70 units. In a conventional transaction, the shipper loses sales of 30 units (carrier capacity shortage) and sells 50 units in excess of the contract through the spot market. The remaining 70 units are shipped by the contract carrier. However, on a shippersÕ exchange, a shipper has advance knowledge of the carrier default and can plan to also ship the default units (30 units) through the spot market. Thus, in this situation, the shipper ships 80 units through the spot market and avoids losing any sales.
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S
ProfitII shippers’ exchange
¼
Z
q
Z
c¼0
þ
Z
1
P c T c þ ðq cÞ P c
s¼q
P
fðs cÞ ðP T s Þghðts Þdts f ðsÞgðcÞ ds dc.
ð8Þ
T s ¼0
The difference between this equation and Eq. (3) is the coefficient for the quantity of shipments sent through the spot market. In the case of the conventional transaction (Eq. (3)), only those shipments above the contract quantity may be sent through the spot market (s q). However, in the case of a shippersÕ exchange (Eq. (8)), a shipper may send all of the goods in excess of carrier capacity through the spot market (s c). In the other cases (Case IB, Case III, and Case IV), there is no difference in the shipper profit equations between the shippersÕ exchange and the conventional approach. In Case IB both the shipper and its contract carrier default on the contract with the carrier capacity being greater than the shipper requirement. The shipper sends its shipments via the carrier (50 units, using the example from Table 1) and pays a default penalty to the carrier (for 20 units) to compensate the carrier for not using all available capacity (20 out of 70 units). In Case III, the shipper defaults on the contract and the carrierÕs availability exceeds the contract quantity. The shipper sends its complete shipment through the contract carrier (70 units) and pays default penalties to the carrier to compensate for the default quantity (30 units). And in Case IV, both the shipper and the carrier honor the contract. Based on this discussion, the outcomes in Cases IB, III, and IV are identical to the corresponding cases using the conventional approach. The following expressions summarize the profits earned under the various assumptions: S ProfitIB shippers’
exchange
¼ S ProfitIB conventional ;
S ProfitIII shippers’
exchange
¼ S ProfitIII conventional ;
S ProfitIV shippers’
exchange
¼ S ProfitIV conventional .
and
The total expected shipperÕs profits ðS Profittotal shippers’ by the sum of all five expressions S Profittotal shippers’
exchange
¼ S ProfitIA shippers’ þS
exchange
exchange Þ
using a shippersÕ exchange is given
þ S ProfitIB shippers’
ProfitIII shippers’ exchange
þS
exchange
þ S ProfitII shippers’
exchange
ProfitIV shippers’ exchange .
The shipperÕs performance under other information sharing options (carriersÕ exchange and neutral exchange) may be similarly stated by integrating the appropriate coefficients in Table 2 under the applicable limits. Given the similarity between obtaining these equations and those described in detail above, shipper performance expressions for the neutral and carrierÕs communities are not presented here. 4.4. Shipper performance comparisons The total expected shipper profits on a shippersÕ exchange less the total expected shipper profits total using the conventional method ðS Profittotal shippers’ exchange S Profitconventional Þ in a low-trust relationship is equal to:
R. Kale et al. / Transportation Research Part E 43 (2007) 21–38
Z
Z
s c¼0
þ
Z
q
ðq sÞ P c þ
s¼0 q c¼0
Z
1
s¼q
Z
Z
P
33
fðs cÞ ðP T s Þghðts Þdts f ðsÞgðcÞ ds dc
T s ¼0
P
fðq cÞ ðP T s Þghðts Þ f ðsÞgðcÞ dts ds dc .
ð9Þ
T s ¼0
The first term represents the difference in shipper performance in Case IA, while the second term represents the shipper performance difference in Case II. As noted above, shipper performance in all other cases (IB, III, and IV) are the same under the two approaches. The first term represents the unfair default earned by the shipper on a shippersÕ exchange (30 units in Table 1) and the net revenues earned by the shipper from sending the shipments not covered by the defaulting carrier via the spot market (20 units in Table 1). The second term indicates that in Case II, a shipper may earn more spot market revenues on a shippersÕ exchange than in a conventional system. Access to real-time information regarding a carrierÕs available capacity explains the differential earnings. On a shippersÕ exchange, the shipper knows in advance that the carrier is going to default on the contract. Hence, a shipper is able to send these default quantities through the spot market (30 units in Table 1). In the case of a high-trust relationship, a shipper only earns fair default penalties from its contract carrier. In this case, the difference between shipper profits on a shippersÕ exchange and using total a conventional approach ðS Profittotal shippers’ exchange S Profitconventional Þ is shown as follows: Z s Z q Z P fðs cÞ ðP T s Þghðts Þ f ðsÞgðcÞ dts ds dc c¼0
þ
Z
s¼0 q c¼0
Z
T s ¼0 1
s¼q
Z
P
fðq cÞ ðP T s Þghðts Þ f ðsÞgðcÞ dts ds dc .
ð10Þ
T s ¼0
The first term represents the difference in performance in Case IA, while the second term represents the performance difference in Case II. As noted earlier, in all other cases (IB, III, and IV) there are no profit differences between the two approaches. The first term represents the spot market revenues earned in Case IA by sending the shipments not covered by the defaulting carrier (20 units in Table 1). The second term indicates that a shipper earns more spot market revenues on a shippersÕ exchange than under the conventional method in Case II. Both Eqs. (9) (low-trust) and (10) (high-trust), yield a positive quantity. Therefore, total S Profittotal shippers’ exchange > S Profitconventional or: Proposition. Shipper performance, as measured by profits, is higher on a shippersÕ community than through the conventional method of transportation management. Other comparisons, between (1) conventional method and neutral community, (2) conventional method and carriersÕ community, (3) shippersÕ community and neutral community, (4) shippersÕ community and carrierÕs community, and (5) carriersÕ community and neutral community, are similar in logic to the comparison discussed above and are briefly outlined in Appendix A. Based on this analysis (a numerical example is presented in Appendix B), in the case of lowtrust relationships, shipper performance in various collaborative transportation networks ranks, from best to worst, as follows: (1) shippersÕ community, (2) neutral community, (3) conventional
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R. Kale et al. / Transportation Research Part E 43 (2007) 21–38
method, (4) carriersÕ community. In high-trust relationships, the ranking is: (1–2) shippersÕ and neutral community (tie), (3–4) conventional method and carriersÕ community (tie). Rankings for carrier performance in low-trust relationships are: (1) carrierÕs community, (2) neutral community, (3) conventional method, (4) shippersÕ community and finally, in high-trust relationships, the ranking of carrier performance is: (1–2) carrierÕs and neutral community (tie), (3–4) conventional method and shippersÕ community (tie).
5. Conclusions, implications and limitations The establishment of private communities on Internet-based transportation networks is a relatively new trend that has met with mixed success. Within industry, there has been uncertainty over the costs and benefits of these communities to shippers and carriers. Through a theoretical model based on assumptions derived from industry executives, this paper suggests that shippers may indeed benefit by establishing private communities. The benefits arise from two sources: (1) the ability to use advance information on available capacity to better use the spot market, and (2) the ability in low-trust relationships to penalize carriers by the full value of carrier defaults, rather than by the fair value. These ‘‘unfair’’ defaults reflect the potential that a carrier may be made worse off by participating in an online transportation community and may explain the hesitation among carriers to participate in such communities. On the other hand, the results show that in high-trust relationships carriers are no worse off by cooperating with shippers in their private communities. The findings suggest that carriers entering into collaborative communities should push for as much transparency in exchange relationships as possible in order to benefit from the communities. In situations where shippers face hesitancy on the part of contract carriers to participate in collaborative exchanges, shippers may need to create incentives for the carriers to participate. This finding conforms with evidence in the trade literature showing that in some cases, shippers pay carrier membership fees for participation in collaborative communities (Cooke, 2001). A number of simplifying assumptions have been made with this model. One of the assumptions is that penalties for non-compliance with contract terms are made through default payments for each shipment in which a default occurs. However, in some collaborative arrangements, default payments may not be assessed on a shipment basis. To a large extent, the results may still apply. For example, suppose the carrier does not have the capacity promised to the shipper. Based on discussions with executives of logistics networks, the shipper may rate the carrier on the networkÕs user feedback system based upon its experience. When other shippers pick carriers for their own transportation needs, they use this rating as one of their selection criteria. Thus, if a carrier defaults with its contract shipper, the carrierÕs future business may be adversely affected. The default penalty can be interpreted, in a broad sense, similar to a ‘‘stockout penalty’’ in inventory management and used to operationalize the negative implications of the carrier not fulfilling its contract terms. A second assumption is that transportation rates are linear with respect to volume. In actuality, a carrier may have a schedule of rates with breaks depending on shipment volumes. Therefore, carrying capacity may not be easily or costlessly transferred between shippers. It is further assumed that the spot market is independent of the two parties under consideration. This assump-
R. Kale et al. / Transportation Research Part E 43 (2007) 21–38
35
tion requires a spot market composed of a large number of shippers and carriers. As a result, small numbers of firms cannot influence the price of the spot market. In practice, however, it may be common that on particular transportation lanes only a handful of carriers offer service, so that one or two carriers can clearly affect the spot market price.
Appendix A. Summary of analytical results (1) S Profittotal shippers’ (2) S Profittotal carriers’ S
exchange
> S Profittotal conventional .
exchange
< S Profittotal conventional ,
Profittotal carriers’ exchange
Profittotal conventional ,
(3) S Profittotal neutral
exchange
¼S
in
relationship,
and
> S Profittotal conventional .
exchange
> S Profittotal carriers’
(5) S Profittotal shippers’
exchange
> S Profittotal neutral
exchange ,
S Profittotal shippers’
exchange
¼ S Profittotal neutral
exchange ,
exchange
low-trust
in a high-trust relationship.
(4) S Profittotal shippers’
(6) S Profittotal carriers’
a
< S Profittotal neutral
exchange .
in
a
low-trust
relationship,
and
in a high-trust relationship.
exchange .
For low-trust relationships, the following rankings are obtained from 1 and 2: S Profittotal shippers’
total > S Profittotal conventional > S Profitcarriers’
and from 3 and 5: exchange , total total total S Profitshippers’ exchange > S Profitneutral exchange > S Profitconventional . Hence, in a low-trust relationtotal total total ship: S Profittotal shippers’ exchange > S Profitneutral exchange > S Profitconventional > S Profitcarriers’ exchange . exchange
For high-trust relationships, the following rankings are obtained from 1 and 2: S Profittotal shippers’
total > S Profittotal conventional ¼ S Profitcarriers’
and from 3 and 5: exchange , total total total S Profitshippers’ exchange ¼ S Profitneutral exchange > S Profitconventional . Hence, in a high-trust relationtotal total total ship: S Profittotal shippers’ exchange ¼ S Profitneutral exchange > S Profitconventional ¼ S Profitcarriers’ exchange . exchange
Appendix B. Numerical example1 The shipper profit expressions found in this article for the various transportation management options were solved using ‘‘Matlab’’ software. A few numerical examples of shipper profits are given below for a shippersÕ community, a carriersÕ community, a neutral community, and the conventional method. Table B1 shows the input parameters used in these examples, while Table B2 shows the baseline case—shipper profits in a conventional transaction. 1 The authors would like to thank Kuldeep Amarnath for his help in solving the equations using the Matlab software.
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R. Kale et al. / Transportation Research Part E 43 (2007) 21–38
Table B1 Input parameters Symbol
Variable
Values
Q T Pc Ps P Ts (random)
Contract size between primary shipper–carrier Per unit cost of transportation Carrier penalty per unit of default Shipper penalty per unit of default ShipperÕs selling price per unit of product Spot market transportation price per unit
S (random)
Primary shipperÕs requirements
C (random)
Contract carrierÕs capacity
100 units $50/unit $20/unit $40/unit $80/unit Normal, mean 100, SD 60 (or 100, 60) Normal, with following values (80, 20), (100, 20), (120, 20) Normal, with following values (80, 20), (100, 20), (120, 20)
Table B2 Shipper profits in a conventional transaction Carrier mean
Shipper mean
IA
IB
II
III
IV
Total
80 80 80 100 100 100 120 120 120
80 100 120 80 100 120 80 100 120
806.61 688.74 259.90 309.92 297.73 120.14 61.42 64.68 27.56
440.59 229.73 55.60 363.51 224.27 61.21 134.13 92.20 27.21
394.92 1291.80 2316.40 242.47 792.20 1417.80 78.31 255.70 457.17
159.85 149.37 57.01 503.76 470.74 179.66 847.68 792.11 302.31
80.95 264.03 471.19 255.13 832.10 1485.00 429.30 1400.20 2498.70
1076.31 1934.93 2900.20 1364.87 2319.31 3143.67 1489.42 2540.21 3285.40
Table B3 Shipper profits on a shippersÕ exchange in a low-trust environment Carrier mean
Shipper mean
IA
IB
II
III
IV
Total
80 80 80 100 100 100 120 120 120
80 100 120 80 100 120 80 100 120
1055.30 873.81 324.32 377.12 354.30 141.18 71.36 74.03 31.26
440.59 229.73 55.60 363.51 224.27 61.21 134.13 92.20 27.21
465.64 1514.70 2691.40 268.52 874.30 1556.00 83.75 272.85 486.02
159.85 149.37 57.01 503.76 470.74 179.66 847.68 792.11 302.31
80.95 264.03 471.19 255.13 832.10 1485.00 429.30 1400.20 2498.70
2202.34 3031.64 3599.52 1768.04 2755.71 3423.06 1566.21 2631.39 3345.51
B.1. Shippers’ exchange In a low-trust environment, shipper profits on a shippersÕ exchange are given in Table B3. Compared to the conventional method, shipper profits are greater in Case IA (as the shipper earns
R. Kale et al. / Transportation Research Part E 43 (2007) 21–38
37
Table B4 Shipper profits on a shippersÕ exchange in a high-trust environment Carrier mean
Shipper mean
IA
IB
II
III
IV
Total
80 80 80 100 100 100 120 120 120
80 100 120 80 100 120 80 100 120
936.51 806.67 306.50 343.15 331.32 134.30 66.20 69.97 29.91
440.59 229.73 55.60 363.51 224.27 61.21 134.13 92.20 27.21
465.64 1514.70 2691.40 268.52 874.30 1556.00 83.75 272.85 486.02
159.85 149.37 57.01 503.76 470.74 179.66 847.68 792.11 302.31
80.95 264.03 471.19 255.13 832.10 1485.00 429.30 1400.20 2498.70
2083.55 2964.51 3581.70 1734.06 2732.73 3416.17 1561.05 2627.33 3344.16
opportunistic defaults and sends all remaining shipments through the spot market) and Case II (as the shipper sends all remaining shipments through the spot market). In a high-trust environment, shipper profits on a shippersÕ exchange are shown in Table B4. Compared with shipper profits on a shippersÕ exchange in a low-trust environment, profits in Case IA are now lower, as the shipper does not earn any unfair default penalties from its contract carrier. B.2. Neutral exchange Since shipper profits on a neutral exchange are identical to shipper profits on a shippersÕ exchange in a high-trust environment, no table is presented. B.3. Carriers’ exchange Shipper profits on a carriersÕ exchange in a low-trust environment are given in Table B5. Compared with the conventional method, shipper profits are less in Case IB, as the carrier earns opportunistic default penalties from the shipper. In all other cases, shipper profits in a low-trust environment are the same as the conventional method.
Table B5 Shipper profits on a carriersÕ exchange in a low-trust environment Carrier mean
Shipper mean
IA
IB
II
III
IV
Total
80 80 80 100 100 100 120 120 120
80 100 120 80 100 120 80 100 120
806.61 688.74 259.90 309.92 297.73 120.14 61.42 64.68 27.56
202.53 161.22 45.01 228.62 177.53 52.72 98.17 78.02 24.32
394.92 1291.80 2316.40 242.47 792.20 1417.80 78.31 255.70 457.17
159.85 149.37 57.01 503.76 470.74 179.66 847.68 792.11 302.31
80.95 264.03 471.19 255.13 832.10 1485.00 429.30 1400.20 2498.70
1644.86 2555.16 3149.51 1539.90 2570.30 3255.32 1514.88 2590.72 3310.06
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Lastly, in a high-trust environment, shipper profits on a carriersÕ exchange are the same as the conventional method, so no table is presented in this final case.
References Bailey, J., Rabinovich, E., 2005. Internet book retailing and supply chain management: an analytical study of inventory location speculation and postponement. Transportation Research E: Logistics and Transportation Review 41 (3), 159–177. Choudhury, V., Hartzel, K., Konsynski, B., 1998. Uses and consequences of electronic markets: an empirical investigation in the aircraft parts industry. MIS Quarterly 22 (4), 471–507. Clemons, E.K., Row, M.C., 1993. Limits to inter-firm coordination through information technology: results of a field study in consumer packaged good distribution. Journal of Management Information Systems 10 (1), 73–95. Cooke, J., 2001. A virtual transformation. Logistics Management and Distribution Report 40 (3), 81–84. Cullen, D., 2001. Look out: donÕt miss the benefit of exchanging freight over the Internet. Fleet Owner 96 (11), 23–26. Daugherty, P.J., Myers, M.B., Autry, C.W., 1999. Automatic replenishment programs: an empirical examination. Journal of Business Logistics 20 (2), 63–82. Esper, T., Williams, L., 2003. The value of collaborative transportation management (CTM): its relationship to CPFR and information technology. Transportation Journal 42 (4), 55–65. Golicic, S., Davis, D., 2003. Hypermediaries in the supply chain: for better or for worse? Business Horizons 46 (3), 77– 82. Hannon, D., 2003. Exchanges are dead, but collaboration is not. Purchasing 132 (18), 47–49. Kumar, K., Dissel, V., Han, G., 1996. Sustainable collaboration: managing conflict and cooperation in interorganizational systems. MIS Quarterly 20 (3), 279–300. Lee, H.G., Clark, T., Tam, K.Y., 1999. Research report. Can EDI benefit adopters? Information Systems Research 10 (2), 186–195. Mukhopadhyay, T., Kekre, S., Kalathur, S., 1995. Business value of information technology: a study of electronic data interchange. MIS Quarterly 19 (2), 137–156. Pinkham, M., 2001. Rough waters for e-logistics. Metal Center News 41 (9), 24–26. Premkumar, G., 2000. Interorganization systems and supply chain management: an information processing perspective. Information Systems Management 17 (3), 56–69. Rabinovich, E., 2005. Consumer direct fulfillment performance in Internet retailing: emergency transshipments and demand dispersion. Journal of Business Logistics 26 (1), 79–112. Strozniak, P., 2003. Collaborative logistics. Frontline Solutions 4 (8), 18–22. Waller, M., Johnson, M., Davis, T., 1999. Vendor managed inventory in the retail supply chain. Journal of Business Logistics 20 (1), 183–203.
Analyzing private communities on Internet-based collaborative transportation networks Rahul Kale
a,* ,
Philip T. Evers b, Martin E. Dresner
b
a
b
Coggin College of Business, University of North Florida, Jacksonville, FL 32224, United States Robert H. Smith School of Business, University of Maryland, College Park, MD 20742, United States Received 22 March 2005; received in revised form 21 June 2005; accepted 11 July 2005
Abstract The establishment of private communities on Internet-based transportation networks is a relatively new trend that has met with mixed success. Within industry, there has been uncertainty over the costs and benefits of these communities to shippers and carriers. Through a theoretical model based on assumptions derived from industry executives, this paper suggests that shippers may indeed benefit by establishing private communities. Further, the results show that in high-trust relationships carriers may be no worse off by cooperating with shippers in their private communities. Ó 2005 Elsevier Ltd. All rights reserved. Keywords: Collaborative transportation networks; Internet business models; Transportation management; Shipper– carrier relationships
1. Introduction The Internet offers a wide range of opportunities for conducting business. Some Internet companies operate as extensions of ‘‘brick and mortar’’ firms (for example, barnesandnoble.com) while others represent types of businesses that would not have been possible prior to the advent *
Corresponding author. Tel.: +1 904 620 1107. E-mail address: [email protected] (R. Kale).
1366-5545/$ - see front matter Ó 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.tre.2005.07.004
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R. Kale et al. / Transportation Research Part E 43 (2007) 21–38
of the Internet (for example, on-line database services such as Lexis-Nexus that deliver electronic text directly to end users). One new business category that has been facilitated by the Internet is electronic marketplaces. Although the best known of these marketplaces, ebay.com, caters mainly to consumers, other electronic marketplaces operate primarily in the business-to-business setting. An electronic marketplace is defined as an inter-organizational information system where buyers and sellers can meet to conduct business. Thus, a marketplace is a form of intermediary that establishes electronic links between buyers and sellers interested in conducting transactions (Choudhury et al., 1998). Internet marketplaces in existence include Covisint in the automobile industry, Travelocity in the airline industry, and The Seam in the cotton industry. Several electronic markets operate in the logistics field, bringing together buyers and sellers in such areas as transportation, warehousing, and manufacturing (e.g., Descartes Systems Group, Manhattan Associates, National Transportation Exchange, Nistevo, Transplace). The purpose of this paper is to model a transportation exchange and demonstrate its potential benefits and costs. In particular, the costs and benefits of ‘‘collaborative communities’’ to shippers and carriers are analytically explored. Collaborative communities are formed within transportation exchanges and facilitate the sharing of information between shippers and/or carriers.
2. Transportation exchanges Transportation exchanges are Internet services that bring together buyers (shippers) and sellers (carriers) of transportation services in order to increase the efficiency of both shipper and carrier operations. One way these exchanges benefit shippers is that they allow a larger number of carriers to bid for shipments, thereby increasing supply competition and reducing prices. Carriers, too, may benefit from transportation exchanges by gaining access to a large pool of shippers, allowing for increased capacity utilization and reducing ‘‘dead-hauls’’. Anecdotal accounts suggest that by 2001, there were as many as 100–250 transportation exchanges operating on the Internet (Pinkham, 2001). As with many other dot-com businesses, transportation exchanges have not been universally successful. Among the reasons most often cited for their failure are that shippers and carriers use the exchanges to transact only a small percentage of shipments and that shippers prefer to send loads via their trusted contract carriers rather than rely on Internet-facilitated spot market exchanges (Pinkham, 2001; Cooke, 2001). As well, carriers are reluctant to participate in transportation exchanges in order to avoid reducing their businesses to ‘‘commodity’’ status, thereby eroding profit margins. To increase their viability, some transportation exchanges have focused on facilitating existing relationships between shippers and carriers, using the market to supplement, rather than replace, existing relationships. A number of success stories have been reported in the trade press (c.f., Hannon, 2003). For example, Transplace.com, a technology-oriented third party logistics provider, manages the transportation for participating shippers by analyzing shipping lanes and identifying opportunities to combine loads across shippers. Transplace.com terms this concept ‘‘collaborative transportation management’’ (CTM). According to Esper and Williams (2003, p. 55), ‘‘CTM essentially involves converting order forecasts developed via CPFR [Collaborative Planning, Forecasting and Replenishment] into shipment forecasts, and insuring their accurate fulfillment’’.
R. Kale et al. / Transportation Research Part E 43 (2007) 21–38
23
A term that is sometimes used to describe Internet-based collaborative services is ‘‘collaborative transportation networks’’. These networks (e.g., NTE.com or Nistevo.com) allow shippers and carriers to create ‘‘private communities’’ to better manage their transportation needs and resources. Depending on the rules of the community, shippers may share shipment information with their core carriers and/or participating shippers, in order to increase capacity utilization and reduce short shipments (c.f., Cooke, 2001; Cullen, 2001). Participating shippers can also identify backhaul opportunities for their contract carriers and, in turn, get a price break from the carriers (Strozniak, 2003). During discussions with industry officials regarding collaborative transportation networks, it was noted that no general rules govern all private communities. In certain instances, the owner of the community sets the rules, whereas in others cases the rules are set through open discussions between the participating shippers and/or carriers. However, based on these same discussions, it was concluded that collaborative transportation communities can generally be classified into three categories as shown in Fig. 1. The first is a ‘‘shippersÕ community’’. Typically, a shippersÕ community is geared towards improving the transportation performance of shippers. Shippers may share information on shipping requirements. If one shipper has extra needs, it can negotiate with a second shipper that has excess contracted capacity, thus creating cost savings for both shippers. The first shipper may receive below-market prices for carrier capacity, while the second shipper may avoid defaulting with its contract carrier for reneging on contracted capacity. With neutral exchanges, shippers and carriers may participate together in sharing information on shipping requirements and capacity availability (e.g., Cooke, 2001). Though these communities may be owned by shippers, neutral communities typically strive to benefit all of the participating parties. Therefore, carriers may achieve higher capacity utilization and shippers fewer short shipments through the sharing of information on neutral exchanges. Finally, it is conceivable that carriers could create carriersÕ communities to manage their relationships with shippers. Although no such arrangements (of which the authors are aware) currently exist, these communities would involve carriers sharing capacity and shipment information for their own benefit.
Shippers’ Community
Carriers’ Community
C1
Neutral Community
S2
C4
S4
S3
C5
S5
C3
S1 C2
S1 Shipper
C1 Carrier
Information flow
Fig. 1. General classification of on-line communities on transportation exchanges.
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R. Kale et al. / Transportation Research Part E 43 (2007) 21–38
3. Literature review and research questions Internet-based collaborative logistics networks are relatively new; consequently, very little academic literature deals directly with such networks. In the only relevant research paper found on the topic, Golicic and Davis (2003) presented a case study based on one such network. Though all of the participating shippers and carriers agreed that there were benefits to participating in these networks, none were able to definitively establish whether they were, on the whole, better off by doing so. Many aspects of collaborative communities are, however, similar to existing literature at the intersection of supply chain management and Internet operations. As a consequence, relevant research efforts from these fields and their implications for collaborative logistics communities are briefly noted below. A private collaborative community formed between a shipper and its carrier base is quite similar to an inter-organizational system (IOS). There have been several studies in the literature showing that information sharing between firms through an IOS results in improved performance. For instance, Internet-based collaboration between online retailers of music compact discs and their vendors may result in reduced inventory levels and cycle time for the same level of customer service. A consumer-direct-fulfillment arrangement between online retailers and their vendors may be facilitated through an IOS (Rabinovich, 2005). As Internet-based retailing facilitates the de-coupling of customer-order locations and inventory locations, online retailers may make better use of inventory location speculation and postponement strategies to improve inventory management (Bailey and Rabinovich, 2005). Electronic data interchange is one of the more common methods of establishing an information sharing relationship between two firms and has been shown to lead to reductions in inventory carrying costs, obsolete inventory, and emergency shipments (c.f., Mukhopadhyay et al., 1995). In addition, an IOS enables various supply chain initiatives such as Vendor Managed Inventory, Quick Response, Continuous Replenishment Planning, and Efficient Consumer Response. These programs have also been shown to result in reduced inventories, higher fill rates, shorter order cycles, reduced transaction costs, reduced transportation costs, and improved customer service (c.f., Waller et al., 1999; Lee et al., 1999; Daugherty et al., 1999). Though an IOS often leads to increased efficiencies, there are some issues that should be carefully considered by firms when evaluating their implementation. While information sharing between firms creates efficiencies, there is the potential for asymmetric benefits in which one firm receives a larger share of the gains than the other. Indeed, one firm may benefit at the expense of the other firm as adoption of an IOS could expose firms to the risks of opportunism or exploitation. A few conceptual papers allude to this possibility (e.g., Kumar et al., 1996; Premkumar, 2000). Given the potential for vastly varying benefits, participating parties may actually resist the implementation to enter IOS-based collaborative efforts (e.g., Clemons and Row, 1993). Examples of collaborative transportation networks creating win–win scenarios for shippers and carriers have been presented in the trade press. Given their potential to add value for shippers and carriers, it is interesting to ask why these networks are not commonly employed. It may be that firms are unaware of the benefits of participating in these networks. Or perhaps, firms are reluctant to share information with competitors, customers, and/or suppliers. In this paper, the conditions under which shippers and carriers are likely to be better (or worse) off by participating in a collaborative transportation network are investigated.
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Based on the above discussion, the following questions are considered: 1. As shippers (and carriers) form private collaborative communities on Internet-based collaborative networks, will such communities create efficiencies in shipper–carrier operations and, if so, by what means? 2. Is there a potential for asymmetric benefits, where one party may gain more than the other, depending upon the information sharing arrangement between them? 3. Is it possible that one party may benefit at the expense of the other? 4. Considering the above aspects, what are the conditions under which private communities are beneficial to shippers and to carriers? 4. Shipper performance model The models presented below are from a shipperÕs point of view. Similar implications arise for carriers. However, to avoid repetitive discussions, those implications will be merely stated where appropriate without presenting modeling details. 4.1. The modeling framework Consider a shipper–carrier dyad where the shipper has fairly stable transportation requirements between a pair of locations. One of the most common methods by which a shipper handles such requirements is to enter into a contract with a carrier. Both the shipper and carrier commit to a particular volume (q units, perhaps based on the average requirements of the shipper) and transportation rate (T per unit). Once the contract is set, day-to-day transactions ensue. A carrier sends a vehicle to the shipper with carrying capacity of q units. The shipper then tenders the shipment quantity of q units. However, occurrences are seldom deterministic. On a given day, either the shipperÕs requirements (S) or the carrierÕs availability (C) may deviate from q. If either C or S is greater than q, the excess may be handled via the spot transportation market. However, if C is less than q, then the shipper suffers in the form of either delayed shipments or lost sales. If S is less than q, then the shipper may end up either paying for unused capacity or paying for default penalties. With a conventional contractual relationship, the carrier becomes aware of the actual shipper requirements only when the carrier arrives at the shipperÕs dock. Similarly, the shipper becomes aware of the carrierÕs actual capacity only when the carrier arrives at the shipperÕs dock. By the time a party is aware of a contract default, it may be too late to take remedial actions. Therefore, the party suffers in terms of either delayed delivery, lost sales, or reduced capacity utilization. For this model, it is assumed that any shipments that cannot be made due to unavailable carrying capacity result in lost sales, while all shipments that do get shipped produce sales revenues (i.e., they are sold at a price of P per unit). It is further assumed that, if a shipper fails to provide q units, the shipper pays a penalty to the carrier for each unit of shipment defaulted (Ps per unit). Similarly, if a carrier defaults on its contracted capacity, it pays a penalty to the shipper for each unit of capacity it is short (Pc per unit). Any requirements or available capacity above the contract quantity are transacted through the spot market, composed of all other shippers and carriers, at a
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R. Kale et al. / Transportation Research Part E 43 (2007) 21–38
transportation rate of Ts per unit. It is assumed that the spot market is made up of a large number of shippers and carriers; thus, the spot market price is independent of the shipping requirements (or available capacity) of any individual shipper (or carrier). To summarize the notation: Contract quantity = q (units), Contract transport price = T ($/unit), Carrier penalty = Pc ($/unit), Shipper penalty = Ps ($/unit), ShipperÕs selling price = P ($/unit), Rs Shipper random requirement ¼ S ðunitsÞ with density f ðSÞ and c.d.f. F ðSÞ ¼ 0 f ðtÞ dt, Rc Carrier random availability ¼ C ðunitsÞ with density gðCÞ and c.d.f. GðCÞ ¼ 0 gðtÞ dt, and RT Spot market transport price ¼ T s ð$=unitÞ with density hðT s Þ and c.d.f. HðT s Þ ¼ 0 s hðtÞ dt. Shipper performance is measured by computing gross shipper profits as follows: Gross profit ¼ sales revenue transportation cost carrier penalty received=paid. 4.2. Conventional transportation management Given the modeling framework described above, there are four possible scenarios, shown in Fig. 2. In Case IA, both the shipper and the carrier default on the contract, however the shipperÕs
C
III
IV
q IB II IA q
S
Fig. 2. Possible shipper–carrier transaction scenarios. Note: The X-axis represents shipperÕs shipping requirements (S), while the Y-axis represents the carrierÕs capacity (C). q is the contract volume between the shipper and the carrier. The roman numerals enumerate all possibilities in a shipper–carrier transaction: IA: Both shipper and carrier default on the contract, however, S > C. IB: Both shipper and carrier default on the contract, however, S < C. II: Carrier defaults on the contract while shipper requirements are greater than the contract. III: Shipper defaults on the contract while carrier capacity is greater than the contract. IV: Both shipper and carrier honor the contract and have requirements/capacity greater than the contract.
R. Kale et al. / Transportation Research Part E 43 (2007) 21–38
27
requirements are greater than the carrierÕs available capacity (a numerical example is given in Table 1). Thus, the shipper loses sales while the carrier pays a default penalty to the shipper for the capacity deficit. Referring to Table 1, the shipper loses sales of 20 units, while the carrier pays a penalty to the shipper for 20 units of lost sales (i.e., the difference between requirements and capacity). Using the gross profit expression, the shipperÕs profits for Case IA in a conventional system is as follows: Z s Z q ¼ fP c c T þ ðs cÞ P c gf ðsÞgðcÞds dc. ð1Þ S ProfitIA conventional c¼0
s¼0
The first two terms measure sales revenues and transportation cost, respectively, while the third term represents the penalty paid by the carrier to the shipper, as the carrier defaults on the contract in this case. In Case IB, both the shipper and the carrier default on the contract. However, in this case, the carrierÕs carrying capacity is greater than the shipperÕs shipping requirements. In the specific example shown in Table 1, the carrier incurs 20 units of unutilized capacity. The shipper compensates the carrier by paying a penalty for these 20 unused units. The following equation gives shipper profits in Case IB: Z q Z q IB fP s T s ðc sÞ P s gf ðsÞgðcÞ ds dc. ð2Þ S Profitconventional ¼ c¼s
s¼0
The first two terms again indicate the sales revenues and transportation cost, respectively, whereas the third term now gives the penalty paid by the shipper to the carrier, as the shipper defaults on the contract in this case. In Case II, the carrier defaults on the contract while the shipperÕs requirements are greater than the contract quantity. Since the shipper knows beforehand that its requirements are greater than the contract quantity, it arranges for spot transportation for the requirements above the contract quantity (in the example, 50 units). However, as the carrier defaults on the contract by 30 units, and since the shipper does not know in advance that the carrier is going to default, the shipper cannot use the spot market for the default quantity and loses sales equal to the carrier default. The carrier pays the shipper a penalty for this loss. The following equation measures shipper profits in Case II: Z q Z 1 ¼ fP c T c þ ðq cÞ P c S ProfitII conventional c¼0
þ
Z
s¼q P
fðs qÞ ðP T s Þghðts Þ dts gf ðsÞgðcÞ ds dc.
ð3Þ
T s ¼0
Table 1 Case examples for shipper–carrier transactions (contract quantity = 100 units) Case
Shipper requirements
Carrier capacity
IA IB II III IV
70 50 150 70 150
50 70 70 150 150
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R. Kale et al. / Transportation Research Part E 43 (2007) 21–38
The first three terms in Eq. (3) represent the revenues, transportation cost, and the default paid by the carrier to the shipper. The last, innermost integral gives the revenues earned by the shipper by placing shipments in excess of the contract quantity through the spot market. In Case III, the shipper defaults on the contract while the carrierÕs capacity is greater than the contracted amount. In this case, due to the shipper default, the carrier loses revenues equal to the shipment price for 30 units. The shipper pays a penalty to compensate for these 30 units of unutilized carrier space. Excess capacity above the contracted amount is sold on the spot market by the carrier (50 units). Shipper profits in Case III are as follows: Z 1Z q III fP s T s ðq sÞ P s gf ðsÞgðcÞ ds dc. ð4Þ S Profitconventional ¼ c¼q
s¼0
Finally, in Case IV, both the shipperÕs requirements and the carrierÕs capacity are greater than the contracted amount. Thus, both are able to honor the contract. The shipper sells the shipments above the contract quantity (50 units) through the spot market, while the carrier sells its excess capacity on the spot market (50 units). The following equation gives shipper profits for Case IV: Z 1 Z 1 Z P IV S Profitconventional ¼ P qT qþ fðs qÞ ðP T s Þghðts Þ dts f ðsÞgðcÞ ds dc. c¼q
s¼q
T s ¼0
ð5Þ
Profittotal conventional Þ
The total expected shipper profits ðS using conventional shipping methods under all cases is given by the sum of all the five expressions: IA IB II S Profittotal conventional ¼ S Profitconventional þ S Profitconventional þ S Profitconventional IV þ S ProfitIII conventional þ S Profitconventional .
The revenue and cost coefficients derived from Eqs. (1)–(5) are shown in Table 2, under the column heading ‘‘Conventional Transaction’’. It is assumed that a shipper will only ship products through the spot market if the spot market price for carrier capacity, Ts, is less than or equal to the profit made by selling the product, P. For the sake of convenience, this is shown in Table 2 simply by multiplying the units that the shipper plans to send through the spot market by the per unit expected revenues earned by the shipper on these excess units. The term E(Profit)spot indicates the expected per unit profit earned by the shipper by shipping excess units through the spot market. 4.3. ShippersÕ community Before examining performance on a shippersÕ community, it is necessary to characterize shipper–carrier relationships as either ‘‘low-trust’’ or ‘‘high-trust’’. A high-trust relationship is one where the shipper–carrier pair has a long standing contract and both parties are concerned about maintaining this relationship. A low-trust relationship is one where the shipper and carrier are focused only on their own short-term performance. As described below, the degree of trust in the relationship will affect default quantities and thus profit performance. By using a shippersÕ community and thereby obtaining information about carrier capacities prior to shipments being tendered, a shipper obtains two benefits. First, the shipper may be able to default its contract carrier without the possibility of recourse (shown in Case IA). In this case,
Case
Conventional system
ShippersÕ exchange
CarriersÕ exchange
Neutral exchange
IA
P * c T * c + (s c) * Pc
P * c T * c + (q c) * Pc + (s c) * (P Ts) * E(Profit)spot (LT) or P * c T * c + (s c) * Pc + (s c) * (P Ts) * E(Profit)spot (HT)
P * c T * c + (s c) * Pc
P * c T * c + (s c) * Pc + (s c) * (P Ts) * E(Profit)spot
IB
P * s T * s (c s) * Ps
P * s T * s (c s) * Ps
P * s T * s (q s) * Ps (LT) or P * s T * s (c s) * Ps (HT)
P * s T * s (c s) * Ps
II
P * c T * c + (q c) * Pc + (s q) * (P Ts) * E(Profit)spot
P * c T * c + (q c) * Pc + (s c) * (P Ts) * E(Profit)spot
P * c T * c + (q c) * Pc + (s q) *(P Ts) * E(Profit)spot
P*cT * c + (q c) * Pc + (s c) * (P Ts) * E(Profit)spot
III
P * s T * s (q s) * Ps
P * s T * s (q s) * Ps
P * s T * s (q s) * Ps
P*sT*s (q s) * Ps
IV
P * q T * q + (s q) * (P Ts) * E(Profit)spot
P * q T * q + (s q) * (P Ts) * E(Profit)spot
P * q T * q + (s q) * (P Ts) * E(Profit)spot
P*qT*q + (s q) * (P Ts) * E(Profit)spot
R. Kale et al. / Transportation Research Part E 43 (2007) 21–38
Table 2 Coefficients for shipper and carrier profit functions
Note: (LT) = low-trust shipper–carrier relationship; (HT) = high-trust shipper–carrier relationship.
29
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R. Kale et al. / Transportation Research Part E 43 (2007) 21–38
both the shipper and the carrier default on the contract, but the shipperÕs requirements are greater than the carrierÕs capacity. In a ‘‘low-trust’’ relationship, a shipper may default a carrier against the contract quantity, instead of the actual shipment quantity tendered by the shipper (see Fig. 3). This is termed an ‘‘unfair’’ default, and in a high-trust relationship this default would not be charged. Using the example from Table 1, in Case IA, a shipper penalizes a carrier by 50 units (i.e., defaulting the carrier against the contract) instead of penalizing the carrier by the fair amount of 20 units (i.e., the actual lost sales for the shipper). A second benefit of shippersÕ communities to shippers is that with the advance knowledge of carrier capacity, a shipper may be able to make better use of the spot market. This may happen when the carrier defaults on its contract and the shipperÕs requirements are greater than the carrierÕs capacity (i.e., in Cases IA and II). In Case IA (Fig. 3), both the shipper and the carrier default on the contract, however the shipperÕs requirements are greater than the carrierÕs capacity. Under conventional contracting, a shipper would lose sales due to the carrier default (20 units, in Table 1 example). On a shippersÕ exchange, the shipper would have prior information on carrier capacity and would, therefore, know in advance that the carrier is going to default on the contract. With this advance knowledge, the shipper can plan to send a shipment equal to the carrierÕs default quantity (20 units) through the spot market, assuming the shipper earns positive profits (P > Ts). The profit equation for Case IA in a shippersÕ exchange can be stated as follows: Z s Z q S ProfitIA ¼ P c T c þ ðq cÞ P c shippers’ exchange c¼0 s¼0 Z P þ fðs cÞ ðP T s Þghðts Þ dts f ðsÞgðcÞ ds dc. ð6Þ T s ¼0
C
III
IV
q IB Carrier default on a shippers’ exchange
Carrier default in a conventional transaction
50 IA
II
70
q S
Fig. 3. Case IA: ShippersÕ exchange vs. conventional method. Note: Considering Case IA from Table 1, the shipperÕs requirements (S) are 70 units and the carrierÕs capacity (C) is 50 units. Thus, both default on the contract though S > C. In a conventional transaction, the carrier would be liable to the shipper for 20 units of lost sales. However, in a lowtrust relationship on a shippersÕ exchange, since the carrier does not know the actual shipper requirements, a shipper may default the carrier by 50 units instead of the fair default of 20 units.
R. Kale et al. / Transportation Research Part E 43 (2007) 21–38
31
The last term (innermost integral) in Eq. (6) represents the revenues earned by the shipper from sending the default quantity through the spot market. In the case of a shippersÕ exchange, the coefficient for the carrierÕs default is (q c) instead of (s c) as it is under conventional contracting. The difference represents the unfair default (q c) (s c) = (q s) which a shipper may earn on a shippersÕ exchange in a low-trust relationship (equal to 30 units in the example given in Table 1). In a high-trust relationship, however, shipper profits on a shippersÕ exchange in Case IA are as follows: Z s Z q IA P c T c þ ðs cÞ P c S Profitshippers’ exchange ¼ c¼0 s¼0 Z P þ fðs cÞ ðP T s Þghðts Þ dts f ðsÞgðcÞ ds dc. ð7Þ T s ¼0
For a high-trust relationship, it is assumed that the default quantity is the fair quantity. The shipperÕs earnings from the spot market are the same as in Eq. (6), while the carrier penalty is the same as in the conventional method (Eq. (1)). In Case II (see Fig. 4), the shipperÕs requirements are above the contract quantity and the carrier defaults on the contract. The shipper may benefit from advance information about the carrierÕs capacity and send all of its goods in excess of carrier capacity through the spot market (80 units in the example), instead of just the quantity above the contract (50 units). The shipper profits in this case are as follows:
C
IV
III
q
Spot market sales on a shipper’s exchange IB
70 IA
Spot market sales in a conventional transaction q
Lost sales in a conventional transaction
150
II
S
Fig. 4. Case II: ShippersÕ exchange vs. conventional method. Note: Considering Case II from Table 1, the shipperÕs requirements (S) are 150 units and the carrierÕs capacity (C) is 70 units. In a conventional transaction, the shipper loses sales of 30 units (carrier capacity shortage) and sells 50 units in excess of the contract through the spot market. The remaining 70 units are shipped by the contract carrier. However, on a shippersÕ exchange, a shipper has advance knowledge of the carrier default and can plan to also ship the default units (30 units) through the spot market. Thus, in this situation, the shipper ships 80 units through the spot market and avoids losing any sales.
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R. Kale et al. / Transportation Research Part E 43 (2007) 21–38
S
ProfitII shippers’ exchange
¼
Z
q
Z
c¼0
þ
Z
1
P c T c þ ðq cÞ P c
s¼q
P
fðs cÞ ðP T s Þghðts Þdts f ðsÞgðcÞ ds dc.
ð8Þ
T s ¼0
The difference between this equation and Eq. (3) is the coefficient for the quantity of shipments sent through the spot market. In the case of the conventional transaction (Eq. (3)), only those shipments above the contract quantity may be sent through the spot market (s q). However, in the case of a shippersÕ exchange (Eq. (8)), a shipper may send all of the goods in excess of carrier capacity through the spot market (s c). In the other cases (Case IB, Case III, and Case IV), there is no difference in the shipper profit equations between the shippersÕ exchange and the conventional approach. In Case IB both the shipper and its contract carrier default on the contract with the carrier capacity being greater than the shipper requirement. The shipper sends its shipments via the carrier (50 units, using the example from Table 1) and pays a default penalty to the carrier (for 20 units) to compensate the carrier for not using all available capacity (20 out of 70 units). In Case III, the shipper defaults on the contract and the carrierÕs availability exceeds the contract quantity. The shipper sends its complete shipment through the contract carrier (70 units) and pays default penalties to the carrier to compensate for the default quantity (30 units). And in Case IV, both the shipper and the carrier honor the contract. Based on this discussion, the outcomes in Cases IB, III, and IV are identical to the corresponding cases using the conventional approach. The following expressions summarize the profits earned under the various assumptions: S ProfitIB shippers’
exchange
¼ S ProfitIB conventional ;
S ProfitIII shippers’
exchange
¼ S ProfitIII conventional ;
S ProfitIV shippers’
exchange
¼ S ProfitIV conventional .
and
The total expected shipperÕs profits ðS Profittotal shippers’ by the sum of all five expressions S Profittotal shippers’
exchange
¼ S ProfitIA shippers’ þS
exchange
exchange Þ
using a shippersÕ exchange is given
þ S ProfitIB shippers’
ProfitIII shippers’ exchange
þS
exchange
þ S ProfitII shippers’
exchange
ProfitIV shippers’ exchange .
The shipperÕs performance under other information sharing options (carriersÕ exchange and neutral exchange) may be similarly stated by integrating the appropriate coefficients in Table 2 under the applicable limits. Given the similarity between obtaining these equations and those described in detail above, shipper performance expressions for the neutral and carrierÕs communities are not presented here. 4.4. Shipper performance comparisons The total expected shipper profits on a shippersÕ exchange less the total expected shipper profits total using the conventional method ðS Profittotal shippers’ exchange S Profitconventional Þ in a low-trust relationship is equal to:
R. Kale et al. / Transportation Research Part E 43 (2007) 21–38
Z
Z
s c¼0
þ
Z
q
ðq sÞ P c þ
s¼0 q c¼0
Z
1
s¼q
Z
Z
P
33
fðs cÞ ðP T s Þghðts Þdts f ðsÞgðcÞ ds dc
T s ¼0
P
fðq cÞ ðP T s Þghðts Þ f ðsÞgðcÞ dts ds dc .
ð9Þ
T s ¼0
The first term represents the difference in shipper performance in Case IA, while the second term represents the shipper performance difference in Case II. As noted above, shipper performance in all other cases (IB, III, and IV) are the same under the two approaches. The first term represents the unfair default earned by the shipper on a shippersÕ exchange (30 units in Table 1) and the net revenues earned by the shipper from sending the shipments not covered by the defaulting carrier via the spot market (20 units in Table 1). The second term indicates that in Case II, a shipper may earn more spot market revenues on a shippersÕ exchange than in a conventional system. Access to real-time information regarding a carrierÕs available capacity explains the differential earnings. On a shippersÕ exchange, the shipper knows in advance that the carrier is going to default on the contract. Hence, a shipper is able to send these default quantities through the spot market (30 units in Table 1). In the case of a high-trust relationship, a shipper only earns fair default penalties from its contract carrier. In this case, the difference between shipper profits on a shippersÕ exchange and using total a conventional approach ðS Profittotal shippers’ exchange S Profitconventional Þ is shown as follows: Z s Z q Z P fðs cÞ ðP T s Þghðts Þ f ðsÞgðcÞ dts ds dc c¼0
þ
Z
s¼0 q c¼0
Z
T s ¼0 1
s¼q
Z
P
fðq cÞ ðP T s Þghðts Þ f ðsÞgðcÞ dts ds dc .
ð10Þ
T s ¼0
The first term represents the difference in performance in Case IA, while the second term represents the performance difference in Case II. As noted earlier, in all other cases (IB, III, and IV) there are no profit differences between the two approaches. The first term represents the spot market revenues earned in Case IA by sending the shipments not covered by the defaulting carrier (20 units in Table 1). The second term indicates that a shipper earns more spot market revenues on a shippersÕ exchange than under the conventional method in Case II. Both Eqs. (9) (low-trust) and (10) (high-trust), yield a positive quantity. Therefore, total S Profittotal shippers’ exchange > S Profitconventional or: Proposition. Shipper performance, as measured by profits, is higher on a shippersÕ community than through the conventional method of transportation management. Other comparisons, between (1) conventional method and neutral community, (2) conventional method and carriersÕ community, (3) shippersÕ community and neutral community, (4) shippersÕ community and carrierÕs community, and (5) carriersÕ community and neutral community, are similar in logic to the comparison discussed above and are briefly outlined in Appendix A. Based on this analysis (a numerical example is presented in Appendix B), in the case of lowtrust relationships, shipper performance in various collaborative transportation networks ranks, from best to worst, as follows: (1) shippersÕ community, (2) neutral community, (3) conventional
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R. Kale et al. / Transportation Research Part E 43 (2007) 21–38
method, (4) carriersÕ community. In high-trust relationships, the ranking is: (1–2) shippersÕ and neutral community (tie), (3–4) conventional method and carriersÕ community (tie). Rankings for carrier performance in low-trust relationships are: (1) carrierÕs community, (2) neutral community, (3) conventional method, (4) shippersÕ community and finally, in high-trust relationships, the ranking of carrier performance is: (1–2) carrierÕs and neutral community (tie), (3–4) conventional method and shippersÕ community (tie).
5. Conclusions, implications and limitations The establishment of private communities on Internet-based transportation networks is a relatively new trend that has met with mixed success. Within industry, there has been uncertainty over the costs and benefits of these communities to shippers and carriers. Through a theoretical model based on assumptions derived from industry executives, this paper suggests that shippers may indeed benefit by establishing private communities. The benefits arise from two sources: (1) the ability to use advance information on available capacity to better use the spot market, and (2) the ability in low-trust relationships to penalize carriers by the full value of carrier defaults, rather than by the fair value. These ‘‘unfair’’ defaults reflect the potential that a carrier may be made worse off by participating in an online transportation community and may explain the hesitation among carriers to participate in such communities. On the other hand, the results show that in high-trust relationships carriers are no worse off by cooperating with shippers in their private communities. The findings suggest that carriers entering into collaborative communities should push for as much transparency in exchange relationships as possible in order to benefit from the communities. In situations where shippers face hesitancy on the part of contract carriers to participate in collaborative exchanges, shippers may need to create incentives for the carriers to participate. This finding conforms with evidence in the trade literature showing that in some cases, shippers pay carrier membership fees for participation in collaborative communities (Cooke, 2001). A number of simplifying assumptions have been made with this model. One of the assumptions is that penalties for non-compliance with contract terms are made through default payments for each shipment in which a default occurs. However, in some collaborative arrangements, default payments may not be assessed on a shipment basis. To a large extent, the results may still apply. For example, suppose the carrier does not have the capacity promised to the shipper. Based on discussions with executives of logistics networks, the shipper may rate the carrier on the networkÕs user feedback system based upon its experience. When other shippers pick carriers for their own transportation needs, they use this rating as one of their selection criteria. Thus, if a carrier defaults with its contract shipper, the carrierÕs future business may be adversely affected. The default penalty can be interpreted, in a broad sense, similar to a ‘‘stockout penalty’’ in inventory management and used to operationalize the negative implications of the carrier not fulfilling its contract terms. A second assumption is that transportation rates are linear with respect to volume. In actuality, a carrier may have a schedule of rates with breaks depending on shipment volumes. Therefore, carrying capacity may not be easily or costlessly transferred between shippers. It is further assumed that the spot market is independent of the two parties under consideration. This assump-
R. Kale et al. / Transportation Research Part E 43 (2007) 21–38
35
tion requires a spot market composed of a large number of shippers and carriers. As a result, small numbers of firms cannot influence the price of the spot market. In practice, however, it may be common that on particular transportation lanes only a handful of carriers offer service, so that one or two carriers can clearly affect the spot market price.
Appendix A. Summary of analytical results (1) S Profittotal shippers’ (2) S Profittotal carriers’ S
exchange
> S Profittotal conventional .
exchange
< S Profittotal conventional ,
Profittotal carriers’ exchange
Profittotal conventional ,
(3) S Profittotal neutral
exchange
¼S
in
relationship,
and
> S Profittotal conventional .
exchange
> S Profittotal carriers’
(5) S Profittotal shippers’
exchange
> S Profittotal neutral
exchange ,
S Profittotal shippers’
exchange
¼ S Profittotal neutral
exchange ,
exchange
low-trust
in a high-trust relationship.
(4) S Profittotal shippers’
(6) S Profittotal carriers’
a
< S Profittotal neutral
exchange .
in
a
low-trust
relationship,
and
in a high-trust relationship.
exchange .
For low-trust relationships, the following rankings are obtained from 1 and 2: S Profittotal shippers’
total > S Profittotal conventional > S Profitcarriers’
and from 3 and 5: exchange , total total total S Profitshippers’ exchange > S Profitneutral exchange > S Profitconventional . Hence, in a low-trust relationtotal total total ship: S Profittotal shippers’ exchange > S Profitneutral exchange > S Profitconventional > S Profitcarriers’ exchange . exchange
For high-trust relationships, the following rankings are obtained from 1 and 2: S Profittotal shippers’
total > S Profittotal conventional ¼ S Profitcarriers’
and from 3 and 5: exchange , total total total S Profitshippers’ exchange ¼ S Profitneutral exchange > S Profitconventional . Hence, in a high-trust relationtotal total total ship: S Profittotal shippers’ exchange ¼ S Profitneutral exchange > S Profitconventional ¼ S Profitcarriers’ exchange . exchange
Appendix B. Numerical example1 The shipper profit expressions found in this article for the various transportation management options were solved using ‘‘Matlab’’ software. A few numerical examples of shipper profits are given below for a shippersÕ community, a carriersÕ community, a neutral community, and the conventional method. Table B1 shows the input parameters used in these examples, while Table B2 shows the baseline case—shipper profits in a conventional transaction. 1 The authors would like to thank Kuldeep Amarnath for his help in solving the equations using the Matlab software.
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R. Kale et al. / Transportation Research Part E 43 (2007) 21–38
Table B1 Input parameters Symbol
Variable
Values
Q T Pc Ps P Ts (random)
Contract size between primary shipper–carrier Per unit cost of transportation Carrier penalty per unit of default Shipper penalty per unit of default ShipperÕs selling price per unit of product Spot market transportation price per unit
S (random)
Primary shipperÕs requirements
C (random)
Contract carrierÕs capacity
100 units $50/unit $20/unit $40/unit $80/unit Normal, mean 100, SD 60 (or 100, 60) Normal, with following values (80, 20), (100, 20), (120, 20) Normal, with following values (80, 20), (100, 20), (120, 20)
Table B2 Shipper profits in a conventional transaction Carrier mean
Shipper mean
IA
IB
II
III
IV
Total
80 80 80 100 100 100 120 120 120
80 100 120 80 100 120 80 100 120
806.61 688.74 259.90 309.92 297.73 120.14 61.42 64.68 27.56
440.59 229.73 55.60 363.51 224.27 61.21 134.13 92.20 27.21
394.92 1291.80 2316.40 242.47 792.20 1417.80 78.31 255.70 457.17
159.85 149.37 57.01 503.76 470.74 179.66 847.68 792.11 302.31
80.95 264.03 471.19 255.13 832.10 1485.00 429.30 1400.20 2498.70
1076.31 1934.93 2900.20 1364.87 2319.31 3143.67 1489.42 2540.21 3285.40
Table B3 Shipper profits on a shippersÕ exchange in a low-trust environment Carrier mean
Shipper mean
IA
IB
II
III
IV
Total
80 80 80 100 100 100 120 120 120
80 100 120 80 100 120 80 100 120
1055.30 873.81 324.32 377.12 354.30 141.18 71.36 74.03 31.26
440.59 229.73 55.60 363.51 224.27 61.21 134.13 92.20 27.21
465.64 1514.70 2691.40 268.52 874.30 1556.00 83.75 272.85 486.02
159.85 149.37 57.01 503.76 470.74 179.66 847.68 792.11 302.31
80.95 264.03 471.19 255.13 832.10 1485.00 429.30 1400.20 2498.70
2202.34 3031.64 3599.52 1768.04 2755.71 3423.06 1566.21 2631.39 3345.51
B.1. Shippers’ exchange In a low-trust environment, shipper profits on a shippersÕ exchange are given in Table B3. Compared to the conventional method, shipper profits are greater in Case IA (as the shipper earns
R. Kale et al. / Transportation Research Part E 43 (2007) 21–38
37
Table B4 Shipper profits on a shippersÕ exchange in a high-trust environment Carrier mean
Shipper mean
IA
IB
II
III
IV
Total
80 80 80 100 100 100 120 120 120
80 100 120 80 100 120 80 100 120
936.51 806.67 306.50 343.15 331.32 134.30 66.20 69.97 29.91
440.59 229.73 55.60 363.51 224.27 61.21 134.13 92.20 27.21
465.64 1514.70 2691.40 268.52 874.30 1556.00 83.75 272.85 486.02
159.85 149.37 57.01 503.76 470.74 179.66 847.68 792.11 302.31
80.95 264.03 471.19 255.13 832.10 1485.00 429.30 1400.20 2498.70
2083.55 2964.51 3581.70 1734.06 2732.73 3416.17 1561.05 2627.33 3344.16
opportunistic defaults and sends all remaining shipments through the spot market) and Case II (as the shipper sends all remaining shipments through the spot market). In a high-trust environment, shipper profits on a shippersÕ exchange are shown in Table B4. Compared with shipper profits on a shippersÕ exchange in a low-trust environment, profits in Case IA are now lower, as the shipper does not earn any unfair default penalties from its contract carrier. B.2. Neutral exchange Since shipper profits on a neutral exchange are identical to shipper profits on a shippersÕ exchange in a high-trust environment, no table is presented. B.3. Carriers’ exchange Shipper profits on a carriersÕ exchange in a low-trust environment are given in Table B5. Compared with the conventional method, shipper profits are less in Case IB, as the carrier earns opportunistic default penalties from the shipper. In all other cases, shipper profits in a low-trust environment are the same as the conventional method.
Table B5 Shipper profits on a carriersÕ exchange in a low-trust environment Carrier mean
Shipper mean
IA
IB
II
III
IV
Total
80 80 80 100 100 100 120 120 120
80 100 120 80 100 120 80 100 120
806.61 688.74 259.90 309.92 297.73 120.14 61.42 64.68 27.56
202.53 161.22 45.01 228.62 177.53 52.72 98.17 78.02 24.32
394.92 1291.80 2316.40 242.47 792.20 1417.80 78.31 255.70 457.17
159.85 149.37 57.01 503.76 470.74 179.66 847.68 792.11 302.31
80.95 264.03 471.19 255.13 832.10 1485.00 429.30 1400.20 2498.70
1644.86 2555.16 3149.51 1539.90 2570.30 3255.32 1514.88 2590.72 3310.06
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R. Kale et al. / Transportation Research Part E 43 (2007) 21–38
Lastly, in a high-trust environment, shipper profits on a carriersÕ exchange are the same as the conventional method, so no table is presented in this final case.
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