Towards collaborative, intermodal hub networks: A case study in the fast moving consumer goods market

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Transportation Research Part E 41 (2005) 567–583

Towards collaborative, intermodal hub networksA case study in the fast moving consumer goods market

Bas Groothedde a,b,*, Cees Ruijgrok a,c, Lori Tavasszy a

a TNO Environment and Geosciences, Van Mourik Broekmanweg 6, P.O. Box 49, 2600 AA Delft, The Netherlandsb Delft University of Technology, Faculty of Civil Engineering and Geosciences, The Netherlands

c Tias Business School, Tilburg University, The Netherlands

Abstract

Collaborative hub networks can provide an answer to the need to decrease logistics cost and maintainlogistics service levels by shifting consolidated flows to modes that are better suited for handling large vol-umes (rail, barge, coastal shipping), so economies of scale can be obtained. This necessity has beenincreased by the tendency of globalization of industries, smaller shipments sizes, high frequencies, andthe fragmentation of flows. Through collaboration the necessary synchronization between expensive butfast and flexible means of transport and inexpensive, but slow and inflexible means can be combined inan intermodal hub network. This paper shows the rationale behind these collaborative hub networks, basedon the literature on the design of many-to-many hub networks. The resulting methodology is explainedthrough presenting the results of the design and implementation of collaborative hub network for the dis-tribution of fast moving consumer goods using a combination of trucking and inland barges. This concept,first proposed by Vermunt [Vermunt, A.J.M., 1999. Multilognet, the intelligent multimodal logistics net-work, an important node in the worldwide logistics net, Vermunt Logistiek Advies v.o.f., working paper(in Dutch)], won the European Intermodal Award of the European Intermodal Association in 2003, andafter extensive research was launched in The Netherlands as a commercial pilot by logistics service providerVos Logistics and barge operator Riverhopper in January 2004.� 2005 Elsevier Ltd. All rights reserved.

1366-5545/$ - see front matter � 2005 Elsevier Ltd. All rights reserved.doi:10.1016/j.tre.2005.06.005

* Corresponding author. Address: TNO Environment and Geosciences, Van Mourik Broekmanweg 6, P.O. Box 49,2600 AA Delft, The Netherlands.

E-mail address: [email protected] (B. Groothedde).

mailto:[email protected]

568 B. Groothedde et al. / Transportation Research Part E 41 (2005) 567–583

Keywords: Hub network design; Collaboration; Intermodal transportation; Network development; Parallel transpor-tation

1. Introduction

During the last thirty years, the imperatives of customer service and cost efficiency have pushedfirms to change their strategy and logistics organization, resulting in centralization of productionand distribution, reduction of inventory and time based competition. Although, for many compa-nies these changes in strategy have been a part of a broader response to growing global opportu-nities and increased levels of international competition, they have led to increased competitionand marketing pressure, dictating shorter lifecycles and order cycle times, a larger variety of prod-ucts and production in smaller quantities (Muilerman, 2001). The evolution of logistics networksduring this period can be characterized by a strong rationalization of business processes. Thisongoing rationalization has led to a constant search for economies of scale and scope in the supplychain, which has been an important parallel development in line with the changes in internationalcompetition and manufacturing. For example, the logistics costs in Europe as percentage of saleshave dropped 40% from 1981 to 2001 (Piper Jaffray, 2002). However, cost efficiency and reliabilityare under pressure due to increasing demands, congestion and vehicle-restrictions. The problemsand inefficiencies in today�s road transportation make it necessary to look for alternatives in thissector. One such alternative could be formed by a collaborative hub network, in which inlandshipping (barge transportation) is used for the inter-hub transportation and economies of scaleare achieved, and parallel to this hub network, direct trucking is used to maintain responsivenessand flexibility. The collaboration between shippers and the carriers in these networks is essentialin order to guarantee the synchronized organization of the network.

In the project Feasibility of Inland Shipping Networks, the problem at hand was the design ofsuch a hub network using barges for the transportation of palletized fast moving consumer goods(FMCG), with the aim to establish economies of scale and scope through collaboration (TNOInro, 2001, 2002, 2003). Retail organizations like Albert Heijn, Schuitema and Laurus and manu-facturers like Heineken, Interbrew, Grolsch, Unilever, Coca Cola, Sara Lee and Kimberly-Clarkparticipated in the project that started in 2000. The objective of the project was to develop andimplement a hub network in which economies of scale and a sufficient level of reliability wasreached through collaboration. In Section 2 of the paper we discuss the concept of a collaborativehub network and illustrate the concept of parallel intermodal transport to achieve economies ofscale by the use of inland shipping and maintain responsiveness by the use of trucking. In Section3 we focus on the retail sector in The Netherlands and in Section 4 we discuss the logistics costs andeconomies of scale present in the hub network. In Section 5 we present the results of the project andour findings concerning the important mechanisms in these collaborative logistics networks are dis-cussed in Section 6. Finally the paper is concluded with the implications for research and policy.

2. The concept of an intermodal hub network

The concept to consolidate palletized flows between manufacturers and retailers, using inlandbarges and form a collaborative hub network, was first proposed by Vermunt (1999). Until then,

B. Groothedde et al. / Transportation Research Part E 41 (2005) 567–583 569

the initiatives for pallet-transport by inland navigation comprised dedicated transportation foronly one participant. Examples of previous initiatives in the Netherlands were the transportationby barge of palletized dairy products of Unilever in 1993, soft drinks of Coca Cola in 1995 andbeer products of Bavaria in 1999. All these initiatives failed, for a number of reasons, but in es-sence these companies could not achieve the necessary scale on their own to operate a cost efficientintermodal network.

The case study, described in this paper, aimed at the development of a hub network in whichthe necessary scale is achieved by combining the flows of different shippers and in which relativelysmall dedicated pallet barges follow a high frequency schedule, with fast and cost efficient trans-shipment on the hubs connecting the inland barges with road transport. These barges areequipped with fully automated pallet positioning and handling systems, minimizing the transship-ment time and costs on the hub and maximizing the annual shipments.

In Fig. 1 two situations are illustrated: the current situation, in which product flows betweenmanufacturing locations and retail warehouses are transported by truck. The problem is that,due to the demands on frequency, drop size and reliability the cost-efficiency is under pressure.To counter this development the product flows of different shippers can be consolidated andshipped through a collaborative hub network, in which economies of scale can be achieved.The extra costs that are introduced because the products are sent through the hub network forextra handling, transportation to and from the hub, should be compensated by the economiesof scale of the inter-hub transportation.

In Fig. 2 the concept is illustrated in more detail. In this figure two alternatives are illustrated;the first is direct transportation from manufacturer to the retail warehouse; the second alternativeconsists of shipment through the hub network making use of barges. When making use of the hubnetwork the products are shipped from the manufacturer to the nearest hub by truck (A); the

Fig. 1. Fragmented flows form manufacturer to retail warehouse and consolidation of flows using a hub network.

Fig. 2. The concept of an intermodal hub network.


pallets are then transshipped on a dedicated pallet barge and transported from the hub near theorigin to the hub near the destination (B); unloaded and finally shipped by truck to the final des-tination (C). As these barges have a capacity of 600 pallets (about 20 truckloads) economies ofscale can be achieved on this segment of the intermodal route. It is however essential to combinethis intermodal route with direct trucking that provides logistic services for short distances andexcess demand that cannot be accommodated through the hub network (D). In other words: inthis concept the combination is made between the capacity of inland barges and the responsive-ness and flexibility of road transport; economies of scale and scope can thus be guaranteed. The

Fig. 3. Principle of parallel intermodal by peak shaving.


two modes operate in parallel and make it possible to accommodate the large and predictable vol-umes and the peaks in demand.

Because the shipment time increases considerably when barges are used the order lead-time willusually be exceeded if the order is shipped after it is received. On average it takes 10–15 h to cover150 km using these barges. To cover this same distance by truck would take approximately 2 h.This gap (t4–t3, depicted in Fig. 3) between the shipment time via the hub network (t4–t1) andthe order lead-time (t3–t1) forms a great problem, especially in the retail where lead-times of 3 hare no exception. To avoid this problem, the shipment should be sent in advance, before the orderis placed. The part that can be well forecasted can be sent through the hub network in advance, theunpredictable part of demand can be accommodated by direct trucking (see Fig. 3).

3. The fast moving consumer goods market in The Netherlands

Retailers in The Netherlands are being faced with many significant changes today; increasedcompetition is creating pressure on retailers to simultaneously control cost and improve customerservice. The market for this segment of products, dominated by a relative small number of retail-ers in The Netherlands, has great potential for the start of a collaborative hub network. First ofall, high volumes and frequent shipments make this segment very attractive for a hub network.Second, as the number of retailers is limited, the flows can be consolidated relatively easily.And finally, the problems in today�s road transportation concerning reliability and costs arebecoming ever more prominent making the chances for an alternative mode of transport morefavorable. In addition to these three drivers, not only the limited number of retailers plays animportant role, upstream in the market, the number of manufacturers in The Netherlands is re-duced significantly due to mergers and acquisitions and more and more product categories aredominated by only a few companies.

In order to comprehend the possibilities of a collaborative hub network an extensive marketanalysis was first performed. This analysis, which examined the Dutch retailers and manufacturersin the fast moving consumer goods market, resulted in an estimate of the potential market, map-ping the production, incoming product flows of the warehouses and pallet transportation, brokendown into 28 product categories. Together, these manufacturers and retailers transport a volume of26.6 million pallets annually between the mentioned 250 production locations and 210 distributioncenters (TNO Inro, 2003). In total, over 95 companies were involved in the project and the infor-mation provided by the manufacturers and retailers made it possible to make a relatively accurateestimate of the pallet flows between the manufacturing location and the retail distribution centers.

4. Quantifying economies of scale in hub networks

The hub network is designed for many-to-many distribution problems (Daganzo, 1999; O�Kellyand Miller, 1994) in order to reduce logistics costs and as O�Kelly and Bryan (1998) so aptly put it:economies of scale, due to the amalgation of flows, provide a raison d�etre for hub systems. Consol-idation in these networks allow more efficient and more frequent shipping by concentrating largeflows onto relatively few links between hubs. Although use of indirect (that is via a hub) shipments


may increase the distance traveled and extra handling increases the costs, the economies of scaledue to the larger volume shipments can reduce total cost. These configurations reduce and sim-plify network constructions costs, centralize commodity handling and sorting and allow carriersto take advantage of economies of scale (Campbell et al., 2002; Eberly et al., 2000). According toHorner and O�Kelly (2001) a cost function ought to be responsive to flow, and should reward thevolume on links by giving them a lower rate. By simplifying inter-hub, hub operational, and han-dling costs and assuming that these costs are independent of flows, the current models may notonly miscalculate the total network costs, but may also erroneously select optimal hub locationsand allocations. In this paper we incorporate the cost functions suggested by O�Kelly and Bryan(1998) for inter-hub flows but in addition the economies of scale on the hub nodes themselves areincluded by making the costs functions on the hubs responsive to flow (e.g. handling, transship-ment), which is especially important in intermodal hub networks, where transshipment costs are adominant cost driver.

4.1. The design problem

The hub network design problem, in its general form, involves: (1) finding the optimal locationsfor the hub facilities; (2) assigning non-hub origins and destinations to the hubs; (3) determininglinkages between the hubs; and, (4) routing flows through the network. The standard hub networkcan be defined as the product of three simplifying restrictions: (1) all hubs are fully interconnected;(2) all nodes are assigned to one hub; and, (3) there are no direct connections between non-hubnodes (Fig. 4).

When optimal shipment sizes are used on all links, the cost on each link is a concave function offlow, and the least cost strategy is always ship all parts direct or ship all parts via the hub network(Blumenfeld et al., 1985; Newell, 1980). However, when there are restrictions (capacity, frequencyand shipment size) on the inter-hub links connections this all or nothing principle can not be used.In the network design problem discussed in this paper we relax all three restrictions; (1) hubs arenot fully interconnected; (2) nodes can be assigned to more than one hub; and, (3) direct connec-tions between non-hub nodes are allowed. This network is called a Protocol H hub networkaccording to the classification system presented by O�Kelly and Miller (1994).

Fig. 4. Considered hub network (Protocol H network).


4.2. Cost functions

In addition to the product flows between the different firms, an important aspect of the projectwas to gain insight into the logistics costs of pallet transportation via the hub network and comparethese costs with the current situation (e.g. direct trucking). The current rates were provided by sev-eral participants, making it possible to compare and calibrate the cost functions used in the project.In the design problem discussed in this paper we distinguish costs for logistics activities like, han-dling, loading, unloading, inter-nodal transport, inter-hub transport, ordering and inventory costs.

We consider a situation in which there are i = 1, . . . , I origins. Each origin can transport itsproduct to each of J destination indexed j = 1, . . . ,J. The volume of demand for product i by des-tination j is denoted dij (e.g. the origins i represents the manufacturing locations, the destinations jrepresents the retail warehouses and dij the flows between both locations). The cost of shipping dijunits directly from origin i to destination j is denoted cij. As an alternative to direct shipment thereare k = 1, . . . ,K hubs or terminals of which a = 1, . . . ,A sequences can be made from the hub nearthe origin to the hub near the destination. So a � k forms a sequence of hubs k = 1, . . . ,K sub-jected to a = hk1, . . . ,kn,k1i. This restriction was included to accommodate the returns and pack-ing and to simplify the implementation process: the first hub in the sequence must also be the lasthub in the sequence completing the cycle.

The cost of shipping between the hubs on sequence a, denoted c�a, is based on the time that theshipment is on sequence a and the actual transportation time (total time minus the transshipmenttime at other hubs) between the origin hub and the destination hub. The total utilization of thesequence la determines the costs per item shipped through the transportation network. If ship-ments are sent via the hub network, additional transit time result in longer travel times, whilethe products wait to be consolidated. Therefore, an additional inventory holding cost is incurredif a shipment from origin i bound for destination j is sent via the hub network. In addition, costsfor handling at the hubs cth, transportation from and to the hubs (cik, ckj) and planning and con-trol cp&c are incurred.

The total logistics costs in network, Cnij, can be formulated as follows:

Cnij ¼

XI

i¼1

XJ

j¼1

cijaijdij þXI

i¼1

XJ

j¼1

XA

a¼1

c�að1 � aijÞdij ð1Þ

In this formula (1), aij denotes the fraction of demand dij that is transported directly from origin ito destination j and (1 � aij) in the second part of formula (1) denotes the fraction that is shippedvia the hub-network. The fraction aij of demand that is sent through the hub network via sequencea, based on the capacity available on this sequence, is calculated if cij > c�a. The shipment costs ofdirect trucking between i (manufacturing location) and j (warehouse) comprise the fixed costs pershipment, and the variable cost. For this direct trucking an average shipment size is assumed. Ifthe costs for direct trucking between origin and destination are calculated (and calibrated usingactual costs information), the costs for shipping via the hub network are calculated. These costs,denoted c�a, comprise the fixed costs ðc�f Þ per time unit multiplied by the total shipment time (Ta).The variable costs per time unit ðc�vÞ multiplied with the transport time (ta), and the utilization ofthe capacity available on sequence a, denoted la.

c�a ¼ c�f T a þ c�vta� �

la

� �þ cik þ ckj þ cth þ cinv þ cp&c ð2Þ

Fig. 5. Representative cost breakdown of intermodal transport in dedicated pallet hub network. For an averageshipment size (25 pallets), using an 89% utilized network with a distance between origin and destination of 152 km.


The costs for transport from origin i to hub k, denoted cik, and transport from hub k todestination j, (ckj), are calculated using the same methodology used to determine the direct roadtransportation. Next, additional costs are incurred for the handling of the products on thehub (cth), inventory costs (cinv), and costs for planning and control (cp&c). An importantdeterminant of the costs for transportation via the hub network is the utilization factor la, avariable dependent on the capacity that is available on sequence a, and the utilization of thiscapacity.

An illustration of a representative cost breakdown of direct trucking versus the costs of ship-ment through the hub network is illustrated in the figure below. For both alternatives the differentcomponents are illustrated. For direct trucking the annual fixed costs and the labor costs both areabout one third of the costs per pallet. For shipment through the hub network the fixed costs(including labor) only make up for about 25%. The transshipment costs at the hub and the roadtransportation (shipping the pallets to and from the hub, depicted A and C in Fig. 2), dominatethe costs with 49% of the costs per pallet. This illustrates the necessity of cost-efficient and fasttransshipment at the hub and minimization of the distance between the origin and destinationand the hub (Fig. 5).

4.3. The development and implementation of the network

In this specific project the development path of the network was as important as the optimalsolution. In order to make a successful implementation of this new concept possible it was essen-tial to start the development (and implementation) of the hub network with a feasible andcost-efficient network, and as the available capacity in dedicated pallet-ships at the start of theimplementation process was limited, capacity restrictions were taken into account in the solutionmethodology. In order to convince manufacturers, retailers and logistic service providers of thepotential cost savings that could be achieved using this new concept, the start of the hub networkshould already yield cost savings. The methodology that was developed could be best described as

Fig. 6. Starting solution: a two hub network using one barge.


an improvement heuristic; it starts with feasible and cost efficient solution and seeks to improveupon it1. Fig. 6 depicts an example of a start solution of a network between two hubs, formingsequence a = hk1,k2,k1i. In this figure the thickness of the line between two hubs indicates the po-tential cost savings if a barge line between both hubs is implemented, using one barge with a uti-lization of la = 0.95 and lti

th ¼ 0:95 (both barge operations and handling equipment have highutilization). In other words: the cost savings for all ij are calculated in the most favorable situationfor these origins and destinations, using the shortest path between two hubs with a highutilization.

If, in this situation the costs for direct trucking are still less than shipment via the hub networkðc�a > cijÞ the hub network will never be an efficient alternative so those origin-destination rela-tions are discarded. The first step consists of finding the best 2-hub connections (the thick darkline in Fig. 6), the next step consists of increasing the capacity by adding another barge and cal-culating the new costs costs/pallet for all ij and compare these new costs, based on the all invest-ments, labor, variable cots and equipment needed, with the costs of direct trucking from thebaseline. If no improvement is found a search is started to extend the network, not by addingcapacity, but by adding a hub to the 2-hub sequence (Fig. 7). For every extension the costs forall origin and destination relations are calculated and again compared with the costs of directtrucking. Only those OD-relations for which the hub network is cost-effective will make use ofthe network. If finally no improvements are found by adding capacity or hubs, the developmentof sequence is finished.

In Fig. 8 the development path of a specific sequence is presented (e.g. the result of the proce-dure described above for one sequence). In this figure the development of the average reduction inlogistics costs per item of shipments using the hub network is illustrated. In the subjoined table thenumber of hubs in the sequence, the number of barges (capacity) and the origin-destination

1 See Sridharan (1995), Klincewicz et al. (1986), Neebe and Khumalawa (1981), Mickey and Thomas (1987),Abdinnour-Helm (2001), Ernst and Krishnamoorthy (1996), Skorin-Kapov and Skorin-Kapov (1994) and Eberly et al.(2000).

Fig. 8. Reduction in logistics costs per item during the development of the sequence. Illustrated by the number of hubs,barges and OD-relations in the sequence.

Fig. 7. Adding a hub to the sequence.


relations are depicted. In the starting solution a reduction of 2% is realized with a 2-hub systemusing only one barge. For 15 OD-relations2 this network would result in a cost decrease. The next

2 In this paper we frequently use the expression origin-destination relation, with the abbreviation OD-relation. An OD-relation is a flow of one type of product from a manufacturer to a retail warehouse.


step in the development of this network is formed by an increase of the capacity on the networkmaking it possible for 23 relations to reduce their logistics costs by 3% on average. In the next stepof the development a hub is added to this sequence.

This 3-hub sequence then forms a competitive hub network, with an average decrease of 5% inlogistics costs, for 29 OD-relations, compared with direct trucking. If the network is extended an-other hub is added, resulting in an average decrease of approximately 15% in logistics costs for 65OD-relations. Finally for 79 OD-relations an average reduction of 20% in logistics costs realized.This is the natural limit of this sequence, after this step in the development no adjustments (inhubs or barges) result in a decrease in system costs.

In Fig. 9 the development of the cycle time (between barges) is illustrated. The starting networkis a 2-hub network with 1 barge. This network has a cycle time of 46 h. If, in the next step, a bargeis added to the sequence the cycle time is reduced to about 24 h.

The third step consists of an additional hub in the network, which leads to a longer routebetween three instead of two hubs, and therefore a longer cycle-time. If additional barges areadded to the sequence the cycle-time is further reduced. In the final design the cycle-time is10 h. In the same figure the development of the throughput in pallet per year of sequence 1 is illus-trated. Starting with about 100,000 pallets per year, this sequence develops towards an annualthroughput of about 500,000 pallets with six barges between the four hubs. In the table belowthe characteristics of all sequences are presented. These 11 sequences make up for the final design(Table 1).

Development of sequence 1

0

5

10

15

20

25

30

35

40

45

50

1 2 3 4 5 6 7 8

Development of sequence

Cyc

le-t

ime

betw

een

barg

es (

hour

)

0

100

200

300

400

500

600P

alle

ts p

er y

ear

(x10

00)

Cycle time Annual throughput

Fig. 9. Development of the cycle-time between barges of sequence.

Table 1Characteristics of the 11 sequences that form the final design

Sequence Throughput(·1000pallets/year)

Direct truckingcosts(euro/pallet)

Hub networkcosts(euro/pallet)

Delta(euro/pallet)

Total annualsaving(millionsof euro)

Cycletime (h)

Numberof barges

Numberhubs

1 499 21.7 20.5 1.2 1.69 10:00 6 42 502 16.2 13.6 2.6 2.02 8:00 7 53 477 16.1 13.0 3.1 2.16 8:00 7 54 495 14.9 12.0 2.9 2.09 10:00 7 55 494 15.5 12.2 3.4 2.31 6:00 8 46 325 17.1 15.5 1.6 1.05 10:00 6 57 356 15.5 14.2 1.4 1.03 10:00 6 58 466 38.2 36.1 2.1 2.99 10:00 6 59 394 28.4 23.9 4.5 2.55 9:00 7 410 530 28.3 22.8 5.5 4.22 10:00 6 411 330 26.3 21.1 5.2 2.46 12:00 6 5


5. Results of the project

In the previous section the development of a single sequence was illustrated and finally the 11sequences were presented. This final design, based on the pallet flows of all manufacturing loca-tions and retail warehouses is illustrated in Fig. 10. In this design seven national operating se-quences (in The Netherlands) and four international sequences are included. The internationalsequences connect hubs located in The Netherlands with hubs in Germany and Belgium.

The separate sequences are depicted by the thick gray scaled lines connecting the hubs nodes.The remaining manufacturing locations and retail warehouses, not connected to the sequences ofthe hub network, illustrated in the figure above, are still serviced by direct trucking; the hub net-work does not form a cost-effective alternative for all origins and destinations. A total of 4.87 mil-lion pallets would be transported annually by the sequences represented in Fig. 10 and a totalsaving of about EUR 24.51 million a year could be achieved. The total market in this hub networkconsists of 26.6 million pallets. In other words, 18% of the potential 26.6 million pallets would befacilitated by the collaborative hub network3.

Based on the included manufacturing locations and retail warehouses (the OD-relations) thetransportation distance of OD-relations that make use of the hub network averages about168 km. In the resulting network essentially only direct trucking is used for OD-relations upthrough 100 km. The hub network is intensively used over 120 6 dij 6 220 km. Annually, on aver-age 442,000 pallets are transported on these sequences, with an average saving of approximatelyEUR 3.1 per pallet (in comparison with direct trucking), which means an average reduction oflogistics costs of 14% for those OD-relations that make use of the hub network. The results also

3 An indication of the lower bound was found by implementing a 2-hub sequence between the two nearest hubs for allOD-relations and calculating the costs for shipment through this network, under the assumption that the utilization ofthe barges and the equipment on the hub is very high (la = 0.95 and lti

th ¼ 0:95). In this hub network about 35% of allflows would go through the hub network because the lower logistics costs in comparison to direct road trucking.

Fig. 10. The collaborative intermodal hub network design.


revealed that the lead time between vessels varies between 6 and 12 h. An analysis of the possibil-ities of cross-border pallet transportation was also conducted; resulting in four international ser-vices illustrated in Fig. 10 (between The Netherlands, Belgium and Germany). In contrast to theDutch domestic market, in which barges carrying 550 pallets can be used, 1100-pallet barges canbe deployed for these routes. The inland waterways to and from Belgium and Germany are highquality waterways and allow for the deployment of large barges subjected to minimal time loss,due to bridges, floodgates or locks, the number of trips per year and thus the number of palletsper sequence can be substantially increased in comparison with domestic transport.


6. Making hub networks work: towards new collaborative structures

The main motivation behind the tendency to look for collaboration between partners in logisticnetworks is achieving economies of scale and scope (Zinn and Parasuraman, 1997; Gulati et al.,2000). Through the combination of activities it is possible to share costs, through sharing of infor-mation it is possible to avoid unnecessary costs and through avoiding sub-optimization and actingas one organization the business units that co-operate can work more efficiently and become moreeffective at the same time.

In many cases the easiest start for collaboration is to look for possibilities of sharing fixed as-sets. The parties involved in the collaboration have already invested in these resources andthrough maximizing the number of users of theses fixed assets, the cost per unit will drop if thetotal capacity exceeds the demand for these facilities. But also in this simple form of collaborationissues of trust and fear for dependency can form blocking factors. In Fig. 11 the development ofthe logistics costs in the network is illustrated and it is shown that for each of the individual par-ticipants the cost of working on their own is higher than would have been the case if they wouldhave chosen an alternative system.

If the participants decide to share resources one can expect the cost per unit to drop consider-ably. There is however a natural limit to this cost decrease. First of all, the law of diminishingreturns applies and secondly there are problems that are likely to arise if the number of partici-pants in the consortium increases. This is indicated as the natural threshold in Fig. 11. Onecan imagine that this threshold is not only due to the fact that it becomes more difficult to agreeon issues of co-operation and investments with many partners, but also that there will be difficul-ties in allowing someone to enter a successful partnership (the old participants will require fromthe new participant an entry fee that compensates them for the initial investments and for theopportunity to share the profits with them). If the marginal decrease of profit gets lower and

Fig. 11. Development of costs per item, and a threshold in the development of a hub network using a 3PL.


the entrance fee gets higher, a natural limit will be reached. In such a case it makes sense to expectsome third party in logistics services (3PL), to step in. A 3PL can neutralize potential conflicts ofinterest between the participants, is specialized in finding suitable clients for the system and alsocan act as chain manager that organizes the logistics organization for the whole system, takinginto account the different customer requirements of each of the clients.

In fact this is exactly what happened in the case study described in this contribution. There thelogistic service provider (Vos Logistics) took the initiative to commercially exploit the concept andoffered their services to the manufacturer shippers not only with the obligation to find otherclients but also to organize the process of direct trucking, barge transportation and pre- andend-haul on their behalf. What we can learn from this example is that in finding an optimalconfiguration of collaborating partners in a multimodal network there are two complementingprinciples: (1) try to find partners that share mutual interests and are willing to invest in thecollaborative network and (2) find a professional service provider that tries to combine the inter-ests of participants in a (multimodal) network (Underhill, 1996).

7. Implications for research and policy

In the paper we discussed the trends and drivers that make it necessary for manufacturers andretailers to constantly search for economies of scale. In certain situations a collaborative hub net-work can form a solution for the problems in trucking today; through network collaborationeconomies of scale and scope in logistics networks can be achieved. The use of a relatively slowmode of transportation does not automatically mean that the lead time increases. If a combina-tion is sought between inland shipping and road transport scale and scope can be achieved andresponsiveness and flexibility can be guaranteed through road transport; in multimodal networksthe primary objective is not to find the best mode in or/or type choice situations, but to find thebest mix of modes in and/and type combinations.

The principle of achieving economies of scale and scope through collaboration can be applied inmany types of situations but is especially suitable for multi-modal networks. There are exampleswhere this principle is applied in a global/international network context. One example is the Sonycase described in the Trilog report (Trilog Consortium, 1999). In this case a combination of longdistance container transport by sea is combined with air transport in much the same way as de-scribed in the Distrivaart example above. The demand that can be predicted well in advance usesthe sea mode, and the excess demand uses the air mode. Other possibilities are the combination oftruck transport and rail transport or the combination of truck transport and short sea transport.All these examples have in common that multi-modal network solutions apply alternative solutionsparallel to each other instead of the consecutive usage of the alternative modes of transport that isnormally applied. In order for these concepts to be successful they rely heavily on transparent lo-gistic information systems and the willingness of the partners involved to exchange planning data.

Research should go into a better understanding of: (1) advancing hub network design methodsfor full logistics costs (including coordination costs) in realistic networks and, (2) testing theprinciples of collaborative multimodal networks that were developed here for other situationsin which the same design method could be used. One can think of multi-modal networks forshort sea and rail or for the combination of air and sea transport, (3) the real potential of cost


reductions through collaboration (testing the principles in practice), (4) the mechanisms of collab-oration in hub networks and finally (5) the cross-over possibilities from transport to other sectors(energy).

Acknowledgements

Prof. Jos Vermunt played an indispensable role during the entire project and his suggestionsand comments helped to improve the paper significantly and are greatly appreciated. Thisresearch was supported, in part, by TNO, (the Institute of Applied Scientific Research in theNetherlands), Connekt, (the innovation network for traffic and transport), and the Holland Inter-national Distribution Council. This support is gratefully acknowledged.

Appendix A

The heuristic used to design the hub network and illustrate the development path consists oftwo strategies. The first strategy is to add a hub to the sequence based on the potential cost sav-ings. As said the process starts with the m-best sequences that form the initial open sequences. Thenext step is to insert or add a hub to the open sequence. For all open sequences the m-best inser-tions and additions are evaluated. If the adjustments are selected the new cycle time, the capacityand logistic costs are calculated, resulting in the new system costs ðCnþ1

ij Þ. If an improvement insystem costs is found the sequence is added to the open sequences for further development. If how-ever no improvement in system costs are found, and the capacity not yet increased, an additionalship is added and again the system costs are calculated. If this increase in capacity does not lead toa decrease of the system costs, the sequence is discarded.

Adding a hub results in a new cycle time (tcycle) and therefore the new maximum capacity has tobe calculated (Amax). Next the new costs for transport via the hub network, "ij, are calculated ðc�aÞand compared with the costs for direct road transport (cij). If the total logistics costs in the net-work or system costs Cnþ1

ij < Cnij then the new sequence is saved, if however Cnþ1

ij > Cnij, then one

more adjustment is made and that is increasing the capacity on the sequence by adding a ship. If inthis adjusted sequence with increased capacity Cnþ1

ij > Cnij then the sequence is discarded. If, by

adding capacity, Cnþ1ij < Cn

ij the sequence is saved.Eventually no improvements will be found in the system costs of the open sequences and the

optimization ends. The results of the optimization is a collection of development paths that startwith a 2-hub sequence and end with h hubs and B barges.

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Towards collaborative, intermodal hub networks: A case study in the fast moving consumer goods market