Natalya Rozhnova
email: natalya.rozhnova@lip6.fr

Serge Fdida
email: serge.fdida@lip6.fr

An extended Hop-by-hop Interest shaping mechanism for Content-Centric Networking

published or not. The documents may come

I. INTRODUCTION

Content-Centric Networking (CCN) proposed by Van Jacobson et al. in [START_REF] Jacobson | Networking Named Content[END_REF] has been introduced to cope with the evolution of the internet usage towards massive content distribution. This architecture differs from the traditional host-based communication principle in many ways. One important change is that network addresses are replaced by content names to distribute information in CCN networks. Moreover, extensive in-network caching capabilities are introduced to benefit from the observation of the traffic flowing in the network. As a consequence, data packets can be delivered by any CCN router in addition to the source, according to various caching algorithms that are out of scope of this paper. In CCN, a request issued by a user is called Interest and the part of the associated content is called Chunk. For convenience, we will name a stream of Interest/Chunk pairs a Conversation.

The key property of CCN is that one Interest retrieves at most one data packet. It enforces a flow balance in the CCN network that enables multiple Interests to be issued at once. Interest aggregation is an optimization that provides the ability to reduce the network load when multiple interests from different sources request the same content. This is achieved by aggregating them in the CCN router, forwarding a single interest upstream and carrying downstream a single copy of the associated content.

As a consequence of the CCN design principles, we have to regulate the stream of chunks as well as the stream of interests in order to avoid congestion and to improve network performance. We believe that, because of the specific design of CCN, hop-by-hop congestion control provides a suitable solution to this problem. We were first [START_REF] Rozhnova | An Effective Hop-by-hop Interest Shaping Mechanism for CCN Communications[END_REF], to propose a hopby-hop Interest shaping mechanism exploiting the one Interest -one Chunk rule enforced in CCN. In this scheme, each CCN router controls the future rate of data-chunks by shaping the rate of corresponding Interests it is pushing upstream. CCN routers dynamically adjust their Interest sending rate by monitoring the level of Chunks stored in their transmission buffer. The rate resulting from this operation is called the shaping rate. An important control function is the one used to derive an optimum shaping rate that maintains the queue length around an objective. Various important parameters are calibrated in order to efficiently operate the algorithm as a function of the network characteristics.

The important concern in CCN is related to the ability offered to a user to send interests without any limiting rate factor, creating a risk for buffer overflow and performance degradation. We introduce a new rate-based mechanism aiming to control the interest sending rate of a content receiver (a client or source of interest). The scheme introduces an exchange of control information between the CCN nodes and the client. The CCN nodes periodically send control packets with an explicit rate specifying the maximum sending rate for each Client on the path used by this conversation. In order to derive an optimum explicit rate, CCN routers use a control function based on the shaping rate computed by HoBHIS.

The remainder of the paper is organized as follows: Section II presents work related to congestion control in CCN. Section III provides a short introduction to CCN underlining the most meaningful properties for this work. In Section IV, we describe the hop-by-hop Interest shaping mechanism (HoBHIS) and its operating principles. We discuss in Section V, our tolerance mechanism to control the interest rate of a client when generated in excess and creating a risk for losses. We provide a performance evaluation of the solutions in Section VI. Section VII conludes the paper and proposes future work.

II. RELATED WORK

Hop-by-hop congestion control schemes have been widely studied in the past but only recently in the context of CCN.

Traffic control suited to CCN has been considered lately, mostly to study congestion and packet loss in CCN routers. The existing schemes can be classified into two categories: receiver-based and hop-by-hop mechanisms. The scheme introduced in [START_REF] Carofiglio | ICP: Design and Evaluation of an Interest Control Protocol for Content-Centric Networking[END_REF] is one of the first window-based algorithms designed to control the Interest sending rate of a receiver in TCP-like environments. The proposed mechanism uses Chunks packets and associated timers as relevant signals to regulate the number of Interests sent by the receiver. One serious issue of this approach is to properly set the timeout value. The solution used in [START_REF] Carofiglio | ICP: Design and Evaluation of an Interest Control Protocol for Content-Centric Networking[END_REF] sets the timer to the mean value of RTT, that, unfortunately, does not take into account the variability of content sources. The mechanism was later improved in [START_REF] Carofiglio | Multipath Congestion Control in Content-Centric Networks[END_REF] by adding route labels to the Chunks so that the client can identify multiple paths. CCTCP introduced in [5] also represents a receiver-driven control solution. The authors propose to list in each interest packet the subsequent Chunks the client intends to request. The network nodes indicate whether they have cached this Chunk. Thus, the receiver maintains separate congestion states for different content locations where future interest will be satisfied. A similar predictive approach has been proposed in [START_REF] Braun | CCN and TCP coexistence in the Future Internet: Should CCN be compatible to TCP?[END_REF] where the authors have proposed some improvements to reduce the complexity of the protocol.

As shown in the past, the receiver-driven schemes face a fairness problem in networks with heterogeneous RTTs. Some analysis and improvements can be found in [START_REF] Vojnovic | Global fairness of additive-increase and multiplicative-decrease with heterogeneous roundtrip times[END_REF], [START_REF] Henderson | On improving the fairness of tcp congestion avoidance[END_REF]. As caching is a key feature of CCN, the RTT related to the content sources appears as a crucial parameter for receiver-driven mechanisms. A recent work, [START_REF] Braun | An Empirical Study of Receiver-based AIMD Flow-Control Strategies for CCN[END_REF], compares the performance of these schemes and concludes that the receiver-driven solutions based on timeouts are not suitable for CCN because of the unpredictability of the content locations.

A second category of congestion control mechanism uses hop-by-hop interest shaping rather than letting the receiver infer network congestion using timeouts. HoBHIS [START_REF] Rozhnova | An Effective Hop-by-hop Interest Shaping Mechanism for CCN Communications[END_REF] was the first interest-based shaping mechanism developped for CCN. In [START_REF] Oueslati | Flow-aware Traffic Control for a Content-Centric Network[END_REF] the authors present a fair sharing mechanism where Interests exceeding a Data fair rate are discarded. ICP introduced in [START_REF] Carofiglio | ICP: Design and Evaluation of an Interest Control Protocol for Content-Centric Networking[END_REF] was then extended in [START_REF] Carofiglio | Joint Hop-by-hop and Receiver-driven Interest Control Protocol for Content-Centric Networks[END_REF] where the authors describe a joint hop-by-hop congestion control mechanism that is found similar to HoBHIS. The basic idea developped in this general approach is to shape the Interest of every conversation flowing through a CCN node to control congestion of Chunks packets and improve network performance. Other proposals based on interest shaping have been presented in [START_REF] Yi | Adaptive Forwarding in Named Data Networking[END_REF], [START_REF] Yi | A Case for Stateful Forwarding Plane[END_REF]. The proposed scheme shapes the interests so that the associated data rate is equal to some predefined ratio of the reverse link capacity. Recently, a different solution was proposed in [START_REF] Wang | An Improved Hop-by-hop Interest Shaper for Congestion Control in Named Data Networking[END_REF]. The mechanism identifies the interdependence between the interests and its associated chunks and analyzes the fair resource allocation in presence of bidirectional traffic.

The work related to traffic engineering in CCN has developed recently. However, the various contributions are still fragmented and do not address specific important issues. The aim of this paper is to provide a comprehensive solution for congestion control in CCN.

III. CONTENT-CENTRIC NETWORKING

In this section we provide a short introduction to CCN in order to highlight the most important architectural features useful in the context of our contribution.

Any content requested by a Client (or a source of Interests) can be divided into a number of data packets. In order to request a given piece of content, a Client will have to send an interest packet. This interest packet will be forwarded to a location where the content is stored. It could be the server or a CCN router where the content has been cached. As a consequence, in order to fetch a given content, the Client will have to submit as many Interest packets as the number of chunks that exist for that particular content. We observe that an Interest triggers the reception of a single chunk transmitted on the reverse path used by the one followed by the associated Interest from the Client to the location where the chunk is retrieved. The "One Interest -One Chunk" rule is an interesting property that enforces a flow balance in a CCN network.

Each CCN router supports three different types of storage. The Interests are forwarded to the data source thanks to the Forwarding Information Base (FIB). In addition, the Pending Interest Table (PIT) keeps track of the forwarded Interests so that the chunks can be returned to their requestor. Finally, a huge cache is used to store the chunk packets using any caching strategy in order to reduce the network response time for frequently asked content. As the content can be delivered by any cache in the network, the time elapsed from sending an Interest to retrieving the corresponding chunk is defined as a random variable A(t). We will call this delay the Response delay. In the CCN node model we have proposed in [START_REF] Rozhnova | An Effective Hop-by-hop Interest Shaping Mechanism for CCN Communications[END_REF] and also considered in this study, we introduced a transmission buffer associated with each face, that differs from the cache.

An important optimization in CCN is the ability to aggregate Interest packets looking for the same content when flowing through a given node. As a consequence, a single Interest copy will be sent to the network and will be used to retrieve the corresponding chunk. In this situation, the router updates an existing PIT entry adding the interfaces that have requested the same chunk and where the corresponding Interest was aggregated (namely dropped). Once a corresponding chunk is received, the node copies it to all interfaces from this entry.

IV. HOP-BY-HOP INTEREST SHAPING MECHANISM

In this section, we shortly introduce the control principles of HoBHIS, more detailed information can be found in [START_REF] Rozhnova | An Effective Hop-by-hop Interest Shaping Mechanism for CCN Communications[END_REF]. This proactive mechanism is implemented in each CCN router and performs a sharing of the network capacity among different conversations. We define a congestion in CCN router as the overflow of the transmission buffer associated to an output interface. The congestion manifests by the loss of data Chunks. Interest shaping is used to control the flow of Chunks and regulate it according to the available buffering capacity in every CCN router. Every transmission buffer of a CCN router will be monitored and the Chunk traffic will be regulated in order for the data transmission buffer to converge to a given objective value r. The Chunk traffic can be regulated thanks to the "One Interest -One Chunk" rule that provides an elegant way to control the flow of information.

Let us describe the principle of HoBHIS. Figure 1 provides an illustration of the communication process when shaping is enforced. As can be seen from Figure 1, when an Interest is received, it will be delayed in the CCN router if proved necessary in order to avoid congestion of the transmission buffer of that same node. This delay is obtained thanks to the computation of the associated shaping rate that is derived using the following formula:

γ(t) = min[max[C(t) + h r -e(t) A * (t) , 0], C int (t)], (1)
Where A * (t) is a predicted value of the Response delay variable that corresponds to the delay of the control loop. We introduce h, a design parameter, that has an important effect on the dynamic convergence properties of our scheme towards the objective r. The other notations can be found in Table I.

The shaping rate function (1) consists of two parts. The first one corresponds to the transmission queue capacity available to send the Chunks, C(t), while the second represents the additional rate to adjust, namely increase or decrease, the Interest sending rate and still be able to avoid overflow when facing a control loop of duration A * (t) . Obviously, a good estimate for A(t) is needed in order to reduce oscillations and cope with the potential variability of the response delay parameter.

A. Resource sharing

In this section we provide a solution for an efficient buffer sharing of the network resources, for the case when multiple conversations are active. Let suppose that there are F active conversations going through the router. In the simplest case the buffer capacity can be shared between the conversations as r F . However, this solution does not take into account the situation where one or more conversations do not consume their share. In this case, the queue length will converge to

r = F i=1 [r i ] ≤ r, i = 1..F (2)
Each conversation converges to its own threshold r i . Therefore, as can be seen from Formula (2), the total available capacity may not be completely used. In order to improve its utilization, the rest of the resources should be shared between the active conversations. Our solution to solve this issue is described below. The arrival rate for a given conversation can be estimated by the number of chunks in the queue belonging to this conversation. Thus, the rate of a conversation can be expressed as follows:

ρ i (t) = e i (t) F j=1 [e j (t)] (3)
Where e i (t) is the number of chunks of conversation i at time t and F j=1 [e j (t)] is the total queue length. ρ i (t) represents the ratio of the total queue length occupied by conversation i at time t. Taking into account equation (3), we modify the shaping rate formula in the case of multiple conversations as follows:

γ i (t) = C(t) + h. r.ρ i (t) -e i (t) A * i (t) (4)
It is important to notice here that we only need to monitor the active conversations, those that have at least one chunk in the queue and enforce accounting only for this reduced set. Note also that for the sake of simplicity, we consider Chunk packets of constant size. If it was not the case, the length of a chunk packet will be used to adjust the above equation.

Monitoring all conversations is far too expensive but we are concentrating on the active conversations only, those for which packets are queued in the buffer, to reduce the number of states stored in the router. It has been shown in the past (cf. [START_REF] Kortebi | Evaluating the number of active flows in a scheduler realizing fair statistical bandwidth sharing[END_REF]) that such per flow control is scalable because of the relatively small number of active flows. A conversation is considered to be active if a transmission queue of the router owns at least one packet of this conversation.

B. Dynamic adjustment of the design parameter h

In this section we analyse in details the behaviour of HoBHIS and study the dynamic adjustment of the design parameter h. This parameter is very important because it has a significant effect on the dynamic convergence properties. Let us study it with a simple analytical model.

In this model, we consider a single-conversation going through the CCN node and constant response delay A(t) = A. HoBHIS starts to react only after the reception of a data chunk. As during an initialisation period we do not know the size of data packets, the equation ( 1) can not be used to compute the shaping rate. Thus, the Interest sending rate of any CCN node during an initialisation period will be equal to its maximum possible rate to send the Interests, namely:

γ max,t0 = C S I (5)
where S I is the size of an Interest. The oscillations in the data queue might be large during the initialisation period due to a high Interest sending rate. Two situations are possible: case 1) an incoming Interest rate, γ in , is higher or equal to γ max,t0 , and case 2) γ in is less than γ max,t0 .

In the first situation, the router will send a huge number of Interests into the network that may lead to large oscillations in the data queue. Oscillations due to the initialisation period can not be avoided but the parameter h will provide a mean to control in order to stabilize the system as fast as possible.

For the first case where γ in ≥ γ max,t0 , the solution is to quickly serve the Chunks in excess. Let's call e 0 the number of Interests forwarded by a CCN router during the initialisation period. We know that for the case where e 0 > r:

h r -e 0 A < 0 (6)
Thus, to have γ > 0, we should maintain:

|h r -e 0 A | < C (7)
Thus, we have:

|h| < | CA r -e 0 | (8)
When a first Chunk arrives to the node, we can easily compute h using Formula [START_REF] Henderson | On improving the fairness of tcp congestion avoidance[END_REF]. That allows us to make the queue converge to its objective faster and without future oscillations.

The only problem we can face here is the shaping rate that immediately drops from a large value to a small one. In order to reduce this drop of the shaping rate, we can try to maintain it between γ stable ± δ, where δ is defined from:

|γ(t) -γ stable | ≤ δ (9)
Where δ = * stable value, ∈ (0; 1) that corresponds to δ = γ stable = C in our case. Thus, to maintain γ in these bounds we can use h = h opt . Frequently, for control systems = 0.05.

Let us study the second case where γ in < γ max,t0 . We know that:

h r -e(t) A > 0 (10)
It means that in practice the Interest sending rate γ(t) = min{γ max,t0 , γ in } = γ in . When the first Chunk arrives, the shaping rate γ(t) will be computed and expressed in number of Chunks per second and then should be applied to the Interests. Hovewer, the queue size is still less than the objective value r.

Therefore, the design parameter should adjust the shaping rate γ(t) to γ in until the queue converges. We should also take into account the number of Interests that have been sent during the initialisation period and will be quickly returned as Chunks, e 0 . Therefore,

γ in = C + h r -e 0 A (11)
Thus, the value for h can be derived from this equation and will be: The shaping rate formula allows us to control the congestion of the transmission queue by carefully monitoring the rate of the Interest packets in the CCN nodes. However, there is no mean to prevent a Client from sending Interest at high or excessive rates in order to be privileged and retrieve its content faster. Therefore, it is important to define a tolerance mechanism that, in addition, can control the Clients as well as prevent the loss of Interest packets.

h = (γ in -C)A r -e 0 ( 12
) Client i-N CCN node i i+1 CCN node CCN node γ T i-N γ T i-N = min{γ i-N , γ T i } γ T i = min{γ i , γ T i+1 } γ T i+1 = min{γ i+1 ,γ T x } γ T x γ i-N γ i γ i+1
There exist two basic methods to throttle the rate of a client: rate-based and window-based. Each of them has their own merit and both approaches are feasible in CCN. In the case of window-based, the clients behavior is strictly defined by the presence or absence of Data packets. If no feedback arrives, it will be interpreted as congestion in the network and the client will automatically stop sending. However, windowbased control schemes can lead to traffic bursts. Moreover, if the window is too small, the network resources are not effectively used. An adaptation of this method is investigated in [START_REF] Carofiglio | ICP: Design and Evaluation of an Interest Control Protocol for Content-Centric Networking[END_REF] and represents a variant of Additive Increase Multiplicative Decrease (AIMD) for CCN. As the authors use Data Chunks to increase/decrease the congestion window, the estimation of the response time is crucial for this type of control. In [START_REF] Oueslati | Flow-aware Traffic Control for a Content-Centric Network[END_REF], the authors also choose to use AIMD in order to adjust the Interest sending rate used by clients.

Ideally, Clients should fully saturate the link, send packets and keep the Chunks arriving continuously. To achieve this, the window-based control has to estimate an ideal window size that can be computed based on the bottleneck capacity and the Response Delay. The rate-based control has to know the bottleneck capacity or should receive an explicit rate value from the network in order to adapt its sending rate. One advantage of the rate-based control is that it does not stop sending in absence of feedback, but at the same time the client has to be aware of a specific sending rate. This rate might be known thanks to the introduction of a network feedback mechanism or by using an instantaneous measured value of the input Data rate.

For a CCN client it is important to choose an initial value for the sending rate. It may be a small fixed window for window-based approach or any specific rate for rate-based schemes.

A. Tolerance rate

In this section we present the Explicit Interest Rate control mechanism that defines a tolerance with respect to the interest rate that a Client can generate. A rate-based scheme was preferred to a window-based mechanism such as AIMD because it is independent of the end users strategy. In addition, it allows a more accurate control and better network resource utilization.

We define the tolerance rate as the maximum rate that the clients are not allowed to exceed. The principle of our solution is to adjust the tolerance rate to the rate of the bottleneck node. Every CCN router will control the rate of incoming Interests by periodically sending control packets with an explicit rate field advertised back to the Clients. This explicit rate field aggregates the bottleneck rate on that path up to this router and is advertised upstream towards the source.

In our model, two complementary rates are computed for each individual conversation: the shaping rate and the tolerance rate. Thanks to HoBHIS, every CCN router controls the rate of a conversation by shaping the rate of its associated Interests. In addition, control packets will be exchanged and updated by the routers along the path followed by a conversation, to convey the tolerance rate for every single Client. For sake of simplicity and without loss of generality, we will consider a network topology illustrated in figure 2. Each A(t) seconds, CCN nodes transmit control packets with an explicit rate information back to the client. Each router calculates the maximum allowed rate for the Client and updates this field if it is found smaller than the actual value. As a consequence, the control packets carry the most conservative value for the rate, namely, the maximum allowed rate of the path. It is obviously not necessary to update the explicit rate faster than the delay of the control loop A(t).

B. Computation of the Tolerance rate

Let Formula 13 represent the maximum shaping rate for conversation i at time t. The Interest queue contains Interests packets from many different conversations. The control function used to compute the shaping rate of a given conversation enforces the total Chunks queue length to converge to the objective r defined as a percentage of the buffer capacity.

γ i (t) = min[max[C(t) + h rρ i (t) -e(t) A * i (t) , 0], C int (t)], (13)
As we do not know a delay between the bottleneck router and the client, we can not directly use the shaping rate formula to compute the tolerance rate because it may lead to large Interest queue oscillations. Moreover, the shaping rate formula does not depend on the Interest queue length at time t and so the level of Interests in the queue is not controlled. As a result, it would be possible to have an Interest queue always congested.

Thus, the tolerance rate formula should:1) depend on γ(t); 2) use the filling level of Interest queue at time t.

Respecting the conditions listed above, we propose the following computation formula for the tolerance rate:

γ T = γ(t).(1 - e I (t) B I ) (14)
The factor (1 -e I (t) B I ) should decrease the oscillations due to feedback delay and the variations of the shaping rate. Using formula 14 to calculate the explicit rate value, allows us to maintain the total arrival Interest rate aligned with the shaping rate.

VI. PERFORMANCE EVALUATION

The aim of this section is to analyze, through simulations, the performance of HoBHIS and Tolerance rate mechanism. We have implemented our mechanisms in ns3-based Named Data Networking Simulator (ndnSIM), [START_REF]NS-3 based Named Data Networking simulator[END_REF], that implements NDN communication model.

A. Tolerance mechanism

In this section we analyze the performance of our feedback mechanism that is used to enforce the tolerance rate. The simulation topology is presented in Figure 3. In this scenario, Clients 1, 2 and 3 are asking for the content from Server 1, 2 and 3 respectively. The shaping mechanism is implemented in each CCN node. Every CCN node has a timer set to a Response Delay value and send the control packets according to the algorithm defined in Section V. The servers represent the rest of CCN network and generate the random delay uniformly distributed from 0 to 0.1 s for every Chunk packet. All the clients are not greedy and have the maximum sending rate for 100 Interests/s for Clients 1 and 2 and 25 Interests/s for Client 3 they can not exceed. When the explicit rate value is retrieved by the Clients, they adjust their sending rate to this value. Their rates are updated every A(t) seconds with the values obtained from the control packets. We are interested in observing the Interest and Chunk buffer states in bottleneck node R2. The buffer parameters are: buffer size = 100 Chunks, r = 60 Chunks. The control packets size is equal to the Interest packet size that is around 30 Bytes, the Chunk payload size is set to 1000 Bytes.

The results are shown in Figure 5(a) and 5(b). As it can be seen from the Figures, the total average Chunks queue length converges to the threshold r = 60 Chunks as expected by HoBHIS. We can see that the conversations fairly share the buffer capacity. The Clients 1 and 2 have the same percentage of buffer capacity attributed to them because they are emitting with the same rates. As the sending rate of Client 3 is slower than the rates of other two Clients, it does not need the same amount of buffer capacity that Clients 1 and 2. Thus, we see that the part of router ressources that is not used by Client 3 is attributed to Clients 1 and 2. We observe the same behavior of the Interest queue where the resources are fairly shared between the conversations. Figure 5(c) presents the client rates and the shaping rate of the bottleneck node. The rate of Client 3 achieves its maximum value at 25 Interests/s. As the Clients 1 and 2 are able to emit faster than Client 3, their tolerance rate Fig. 6: Chunk queue length and Interest shaping rate convergence speed as a function of h is higher. The sum of Client rates is equal to the bottleneck node's shaping rate. Finally, as a result, we observe optimal resource utilization and no packet loss.

B. Dynamic adjustment of h

In this section we analyze the convergence rate of our mechanisms. We use a simple mono-conversation scenario and one-node topology presented in Figure 4. The A(t) is considered to be constant for these experiments. We are interested in the convergence rate of the data queue of the bottleneck link and the shaping rate towards their objectives that are r = 60 and γ = C, under different values of h. We study two cases presented earlier in Section IV-B, where the first one causes the big oscillations in the data queue after an initialization period while in the second case the data queue is not sufficiently filled. To generate the oscillations in the data queue during the initialisation period we will use the Client Interest sending rate = 5.000 Interests/s that is higher than γ max in order to use the maximum possible Interest sending rate of a CCN node. For the second case we reduce the Client sending rate to 1.000 Interests/s in order to have γ in < γ max .

The curves 6(a) and 6(b) show the results for the case where the data queue is overloaded due to an excess number of Interests sent during the initialization period. Tuning the value of h we observe the different behavior of the Chunk queue. It is easy to see that the optimum value of h dynamically adjusted thanks to the algorithm presented in Section IV-B provides the best convergence rate to the objective value without oscillations whilst other values of the design parameter do not provide a suitable control. For simplicity, we provide a theoretical shaping rate that may become negative.

The results for the second case are presented in Figures 6(c) and 6(d). Again, h opt provide a suitable convergence speed towards the objective. The small and large h values cause slow convergence and oscillations respectively.

In order to challenge our mechanisms we should compare them with other hop-by-hop solutions presented earlier in the literature, i.e. [START_REF] Oueslati | Flow-aware Traffic Control for a Content-Centric Network[END_REF], [START_REF] Carofiglio | Joint Hop-by-hop and Receiver-driven Interest Control Protocol for Content-Centric Networks[END_REF], but unfortunately the corresponding source codes are not publicly available.

VII. CONCLUSION

We presented a first comprehensive solution for congestion control in CCN networks. This problem is of utmost importance and has not been globally addressed in the past. Our framework is grounded on our original HoBHIS mechanism that was the first introduced to provide a hop-by-hop shaping mechanism. It nicely exploits the flow balance enforced in CCN between Interest and Chunk packets. It mostly consists in monitoring active conversations sharing the transmission buffer of a CCN node face in order to dynamically adjust their Interest sending rate and enforce the Chunk queue length to converge to a defined objective. This mechanism is implemented in each CCN node. We extended the design of HoBHIS in order to address the important concerns that might occur in CCN. We first demonstrated the fairness of resource sharing among competing conversations. Second, we introduced an explicit Interest rate feedback mechanism designed to control the Client behavior and prevent a potential risk of network congestion. Each node will compute a Client tolerance rate that is returned upstream towards the source in order to collect the bottleneck rate of the path. A thorough evaluation was conducted using different simulation scenarios. We observed that the results fully satisfy the design objectives and we can conclude that HoBHIS is an efficient and operational solution to the problem of congestion control in CCN. In future work we would certainly like to consider different types of traffic and services and analyze if differentiating between them will provide additional value.

Fig. 1 :

 1 Fig. 1: Communication process when shaping is enforced

Fig. 2 :

 2 Fig. 2: Network model

Fig. 3 :Fig. 4 :

 34 Fig. 3: Network topology to study the Tolerance rate mechanism

TABLE I :

 I Notations

	e(t)	number of queued Chunks at time t
	e I (t)	number of queued Interests at time t
	e I i (t)	number of queued Interests of conversation i at time t
	B	buffer size
	B I	Interest buffer size
	r	queue threshold
	h	design parameter

C(t) available bandwidth to send the chunks at time t Cint(t) available bandwidth to send the Interests at time t γ(t) shaping rate at time t γ T (t) tolerance rate at time t A(t) delay from Interest to the related content A * (t) predicted delay from Interest to the related content


Gareth Stone
email: g.m.stone@bath.ac.uk

Mickey Howard
email: m.b.howard@bath.ac.uk

Joe Miemczyk
email: jmiemczyk@audencia.com

Andrew Graves
email: a.p.graves@bath.ac.uk

Collaborative ICT Ventures and the Implementation of Responsive Automotive Supply Networks

Keywords: information communication technology, supply chain collaboration, implementation, barriers, responsiveness

This research paper explores the generic barriers which govern supply chain integration. Four automotive cases illustrate the motivations and barriers that emerged during the implementation phase of collaborative ICT ventures. The results indicate the reasons for failure of ICT as the solution to collaborative supply chain integration. We show that ICT in relation to supply chain collaboration (SCC) is still at an embryonic stage of development. Recent examples of ebusiness initiatives are used to develop further a framework of barriers for supply chain integration and collaboration based in the automotive sector. The barriers to successful integration and collaboration identified in this research include system standards, legacy systems and interoperability, cost-benefit sharing, and interorganizational trust. The paper concludes by suggesting that for ICT ventures to be successful in achieving requirements, inter-firm relationships must be a first consideration, certainly before implementing technology.

Biographical notes: Gareth Stone joined the School of Management, University of Bath in 2001 after gaining a broad industrial background in the Aerospace industry. He is involved with the European Commission funded Automotive programme ILIPT (Intellegent Logistics for Innovative Product Technologies). Gareth has lectured in operations management at Southampton Business School and has recently been involved with a 3 year Aerospace research programme UKLAI, (UK Lean Aerospace Initiative) which is partnered with MIT and focused on the implementation of lean processes within the UK Aerospace Industry.

Mickey Howard is a Senior Lecturer in Operations Management at the School of Management, University of Bath. Prior to this he worked for 10 years as a product designer of thermoplastic components in the automotive and retail sectors. His career as an academic began at the University of Bath in 1999 as a research officer for the '3Daycar' programme, which examined build-to-order in the automotive industry. His current research focuses on technology & innovation management, and the implementation of responsive supply strategy.

Joe Miemczyk is Assistant Professor in Supply Chain Management at Audencia, Nantes, France. Previously, he has worked on the 3DayCar programme and was most recently theme leader on the European ILIPT project. Joe has published widely on in the area of automotive logistics and has presented at a number of conferences in operations management, logistics and environmental management.

Andrew Graves is Professor of Technology Management at The University of Bath and directs research into Lean Manufacturing and Agile Production within the IMRC (Innovative Manufacturing Research Centre). He is a mechanical engineer and political scientist and began his industrial career as a trainee manager with the Rootes Group in 1966. At the Science Policy Research Unit at the University of Sussex he undertook research with MIT's IMPV (International Motor Vehicle Programme) on R&D and technology issues and subsequently became European Programme Director to the programme.

Customer demand and collaborative leadership

The failure of several high profile Collaborative Planning Forecasting and Replenishment (CPFR) implementations suggest that supply chain integration is still only a promise in more traditional manufacturing sectors such as automotive. Despite considerable efforts by OEMs and suppliers through initiatives such as Covisint, there are few signs of true customer demand responsiveness and collaborative supply chains based on joint decision making. Despite the existence of success stories [START_REF] Smith | West Marine: A CPFR Success Story[END_REF], this is a surprising revelation, particularly given the coverage in academic and trade press regarding the perceived benefits of information sharing through web-enabled CPFR.

Attempts to adopt a joint planning and decision-making strategy have been largely unsuccessful to date. [START_REF] Stank | Collaborative planning: supporting automatic replenishment programs[END_REF] failed to verify the existence of even broad based performance enhancements related to the implementation of CPFR even though they conceded that overall costs could be reduced. Barriers such as industry structure, silo management and product complexity inherent in the supply network all impact on managers' ability to implement collaborative planning systems which enable synchronisation of responsive demand across automotive supply networks.

Much has been made of the benefits accrued by firms in the FMCG, computer, and retail sectors adopting ICT to boost competitiveness through responsive inter-organisational demand management (i.e. Tesco, Dell, Zara). The automotive industry OEMs have also made significant strides to reduce the order fulfilment cycle using ICT to connect limited areas of the supply network, e.g. component manufacture with vehicle assembly, and vehicle distribution with dealer sales. The industry constantly looks to new frontiers to achieve competitive advantage at a time when over-capacities and eroded profits threaten firms' very existence, for example by following the commonly stated goal to only build vehicles to customer demand. While the argument for shifting away from make-to-stock towards a locate-to-order and build-to-order approach is well established (Holweg, 2005), the success factors governing implementation of interorganizational information systems are neither clear in the literature nor widely disseminated. This is astonishing when the industry depends on the manufacture and distribution of goods which require the synchronisation of hundreds of supply partners, thousands of components, and millions of potential product variants per customer order.

2

Literature review: ICT and inter-organizational IS adoption

The role of ICT across industry supply chains has traditionally focused on the fields of economics, co-ordination, and competition [START_REF] Clemons | Information technology and industrial cooperation: the changing economics of cooperation and ownership[END_REF][START_REF] Konsynski | Information partnerships -shared data, shared scale[END_REF][START_REF] Porter | How information gives you competitive advantage[END_REF]. Economic benefits are related to the reduction in transmission charges and administrative costs, such as $200 million savings by the Chrysler Corporation (Mukhopadyay, 1995). Co-ordination benefits arise from the tight coupling and speed of exchanging information leading to efficiencies between firms such as reduced inventory [START_REF] Stank | Collaborative planning: supporting automatic replenishment programs[END_REF]. Competitive advantage stems from gaining access through ICT to diversified markets and leveraging key resources in new areas using economies of scale and scope (Porter & Miller, 1985) The adoption of information systems not only changes the way a firm competes, but also enables technology to be used as a 'competitive weapon' against other firms [START_REF] Ives | The Information System as a Competitive Weapon[END_REF]. [START_REF] Webster | Networks of collaboration or conflict? Electronic data interchange and power in the supply chain[END_REF] provides a classic description of this phenomenon in the automotive industry. The case reveals the high costs of investing in bespoke EDI at the time and which is used as a competitive weapon to preserve the power of dominant stakeholders i.e. the vehicle manufacturer, thereby locking suppliers into procurement relationships and keeping competitors out of them.

An alternative perspective to ICT as a competitive weapon is an enabler of cooperation that provides a stable platform for inter-organizational collaboration. [START_REF] Ciborra | Teams, Markets and Systems -Business innovation and information technology[END_REF] proposes reforms of the relationships with suppliers, customers and other partners based on a mix of organizational forms: teams, markets and hierarchies where technology can be harnessed to shape businesses and increase market transparency.

Supply chain relationships today have, generally speaking, advanced from competitive to collaborative [START_REF] Bowersox | Logistical Management: The Integrated Supply Chain Process[END_REF]. This is reflected by a shift in inter-organizational systems literature in the 1990s that describes the role of information technology as 'enabling the transition from inter-firm competition to cooperation' [START_REF] Kumar | Sustainable collaboration: managing conflict and cooperation in interorganizational systems[END_REF]. These are described as information and communication technology systems that transcend legal enterprise boundaries and imply a high level of cooperation and coordination. Their classification of system types identifies the arguments for potential conflict and hence possible strategies for containing conflict. Similarly, [START_REF] Hart | Power and Trust: Critical factors in the Adoption and Use of Electronic Data Interchange[END_REF] study electronic relationships between organizations, particularly the role of power and trust in EDI adoption. They posit computer networks are increasingly adopted to support the flow of information with 'their use both influencing and having consequence for inter-organizational relationships'.

It is often argued that ICT will reshape the automotive industry. True, the car business is ripe for revolution being poorly equipped to thrive in the 21st century and with chronic debt and overcapacity problems, where '…about half of the industry is regularly incapable of earning a decent return on its invested capital ' Economist (2004a). Yet several false summits have already been encountered. First, the arrival of Enterprise Resource Planning (ERP) in the 1990's has not proved as successful as first anticipated, with around half of customers across all sectors believing they paid more than the system was worth [START_REF] Vance | SAP costs too much -customers: www[END_REF]. Second, the promise of the e-commerce revolution, driven by visions of virtual organizations and the digital economy [START_REF] Lapidus | Gentlemen, Start Your Search Engines[END_REF]Tapscot et al., 2000), is taking longer to materialise than expected. Despite optimism in business-to-customer (B2C) systems in sectors such as retail, groceries, and music (Economist, 2004b), the adoption of business-to-business (B2B) systems by firms in traditional manufacturing industries such as automotive face considerable barriers (Howard et al, 2006).

In summary, barriers research can be traced to early inter-organizational systems (IOS) literature that considers the interchange of information as the basis of all activity [START_REF] Barret | Inter-Organisational Information Sharing Systems[END_REF]. In their assessment of the impact of IOS Barret and Konsynski describe 'factors of concern' as how new technologies are introduced into the firm and the resultant effects on organizational structure, users, and the IT department. Early attempts to unify the fragmented models of information system implementation reveal five categories of factor: individual, structural, technological, task-related, and environmental [START_REF] Kwon | Unifying the Fragmented Models of Information Systems Implementation[END_REF]. Hence, planning for IOS-related change requires clear strategic objectives and a systematic means of identifying project constraints, barriers, or features [START_REF] Earl | Management Strategies for Information Technology[END_REF]. Even today the planning, implementation, and maintenance of an e-procurement programme across supply chains represents a considerable challenge [START_REF] Presutti | Supply management and e-procurement: creating value added in the supply chain[END_REF]. This situation is exacerbated by automotive industry stakeholders facing a whole range of different barriers to effective change internally and across the supply chain (Howard et al., 2006).

Supply responsiveness

Supply responsiveness can be thought of in two ways. The first deals with how supply chains respond to raw market demand signals. Many automotive manufacturers strive to meet customer expectations through build to order. Supply chains are being engineered to cope with real demands rather than forecast based production. The alternative perspective sees supply chains as needing to deal with fluctuating demand resulting from managerial interventions and incompatible planning and information systems.

Responsiveness in the supply base is needed for both these scenarios. [START_REF] Fisher | What is the Right Supply Chain for your Product?[END_REF] distinguishes between supply chains that are market responsive and those that are physically efficient. The former is aimed at a quick response to unpredictable demand and thus requires high supply chain flexibility. This concept is also specified by [START_REF] Christopher | Global Supply-Chain: The Role of Agility[END_REF] as agility, whereby agility should be obtained through the integration of other elements of the supply chain such as external suppliers. Supply responsiveness, in an automotive context, has been defined as "the ability of a manufacturing system or organisation to adapt to changes and requests in the marketplace" (Holweg, 2005). In order to gain increased supplier responsiveness some authors claim that automotive companies require co-location, single sourcing, selection based on flexibility, internal collaboration (between purchasing and production) and integration with logistics providers [START_REF] Tachizawa | An empirical investigation on supply flexibility[END_REF]. Other sources state process flexibility, e.g. the ability to change schedules on a frequent basis is necessary [START_REF] Krajewski | Responding to schedule changes in build-toorder supply chains[END_REF] and information system flexibility "the ability to align information system architectures with the changing information needs of the organisation as it responds to changing customer demand" [START_REF] Duclos | A conceptual model of supply chain flexibility[END_REF]. Not only do supply chains have to deal with changing demands from the market, but the lack of timely and accurate information exchange causes its own problems. For example, this leads to one of the key topics in supply chain management: the bullwhip effect.

Lee (1997) explains that demand is often amplified up the supply chain due to the way that orders are processed and passed on by each link in that chain. One of the main causes of this effect is demand forecast updating, whereby demand planning information is passed only sequentially up the supply chain, creating large swings in expected demand further upstream. The authors suggest that there are a number of actions that can reduce the bullwhip effect including using Point Of Sale (POS) data, sharing sales, capacity and inventory information, and Vendor Managed Inventory (VMI). Thus a key aspect of integration through ICT is the ability to provide real time information on product availability, inventory levels, shipment status and production requirements and allowing collaborative planning among supply chain partners by sharing information on demand forecasts, production schedules [START_REF] Min | E-purchasing: profiles of adopters and nonadopters[END_REF]. Reconciling actual market demands (which implies wide swings due to seasonality and customer purchasing habits) with volatile demands because of amplification throughout the supply chain, requires a renewed focus on enabling information systems and technologies.

Supply Chain Collaboration

Supply Chain Collaboration (SCC) has raised its profile in supply chain management literature recently and is now being discussed with particular reference to ICT. Indeed, [START_REF] Akkermans | The Impact of ERP on Supply Chain Management: Exploratory Findings from a European Delphi Study, INSEAD[END_REF] suggests that 'focusing on supply chain strategies is the next frontier in organisational excellence'. [START_REF] Frohlich | Arcs of integration: an international study of supply chain strategies[END_REF] agree: the most successful manufacturers carefully link their internal processes to external suppliers and customers. Nevertheless, [START_REF] Ashayeri | Realities of Supply Chain Collaboration, Euroma -Operations and Global Competitivenenss[END_REF] suggest that successful supply chain collaborations are rare, even though SCC is now widely recognised as one of the most pressing needs in supply chain management. In a model scenario, supply chain partners would co-operate to restructure business practices, as necessary using available technology, and provide 'customers with products and services better/faster/cheaper than ever before [START_REF] Stank | Collaborative planning: supporting automatic replenishment programs[END_REF]. ICT is clearly important because historically the automotive sector has relied on forecast information which creates uncertainty and is also influenced by promotions, product variety, changing demand patterns and competitive pressure. Accuracy can be improved by using real-time data [START_REF] Childerhouse | Information flow in automotive supply chains ~ An assessment of present industrial practice[END_REF] and where information from the buyer is important to the supplier's inventory and production decision. [START_REF] Gunasakaran | The Build-to-order supply chain (BOSC): a competitive strategy for the 21st century[END_REF] argues the Build-to-Order Supply Chain (BOSC) is also now being actively pursued in several different industries, and describe studies that support ICT role in implementing them. Yet the adoption of these processes and technologies is problematic, and BOSC may well fail unless practical implementation and firm interoperability can be improved.

The current state of SCC ICT is well documented in the literature; however, a number of initiatives are notable. Quick Response (QR) was implemented in the 1980s in the apparel industry whereby sales data was electronically transmitted to suppliers to trigger production of new items to replenish retailers [START_REF] Hunter | Quick Response in Apparel Manufacturing[END_REF]. Efficient Consumer Response (ECR) was a response to supply chain inefficiencies [START_REF] Barratt | Exploring the Experiences of Collaborative Planning Initiatives[END_REF], where the supply chain was required to transform from a 'stock-push' to a 'customerpull' system, requiring the supply chain to collaborate in order to replenish stock. The concept originates from just-in-time and just-in-sequence strategies typified by current automotive supply chains. ECR cuts costs by eliminating activities that don't add value. Continuous Replenishment Planning (CRP) shares information from the downstream stock situation regarding demand, stock and delivery times, allowing smoother flow from suppliers. The approach allows the supply chain to collaborate by substituting inventory with information [START_REF] Van Hezewijk | Coordination in Chains and Networks, Euroma -Operations and Global Competitiveness[END_REF]. VMI is also a popular supply strategy that is now held in high esteem in a number of market sectors including the automotive and aerospace industries because customers often find it beneficial 'delegate responsibility for the replenishment process to the supply chain' (Disney & Towhill, 2003). In terms of performance, however, the key performance measure is no longer delivery time, but the availability and turnover of inventory. In a similar way, Factory Gate Pricing (FGP) transfers the responsibility and control of replenishment to the retailer with the additional advantage of reduced transportation cost for the customer. Further, the CPFR concept [START_REF] Fliedner | CPFR: an emerging supply chain tool[END_REF] attempts to fill some of the gaps left by previous SCM practices, such as the necessity to hold high stock levels to ensure product availability; multiple forecasts within supply chain; the link between promotions and the sales forecast; changing demand patterns and general synchronisation of supplier efforts.

Based on the VICS process model [START_REF] Barratt | Exploring the Experiences of Collaborative Planning Initiatives[END_REF], the focus of CPFR is enhanced by encompassing planning, forecasting and replenishment processes in one package, where much of its popularity is attributable to the impressive results reported by early programs. However, CPFR takes a number of different forms across supply networks and can be a laborious process to implement, especially when supported by inadequate systems. For example, forecast reconciliation remains a frustrating, errorprone process without automated exception management. Most current solutions also fail to scale adequately as a manufacturer adds more retailers and the problem is made worse by an inability to integrate with back-end production systems. Assumptions are often made over the implementation of ICT projects as being relatively straightforward. Yet this ignores the fundamental problems of sustaining these collaborative ventures across long time periods, involving diverse stakeholders with a variety of multi-level barriers and conflicting motivations.

In summary, the literature is full of examples where IOS adoption is fraught with difficulties, from ERP implementations running over budget, to organizational objectives simply not being met. While earlier literature identifies firm level barriers to IOS adoption, this continues to evolve as new cases of failure emerge. In this research, the focus on supply responsiveness by automotive firms highlights the huge challenges faced in providing appropriate technological and social support for the project. Implementing responsive supply networks through supply chain coordination adds a new layer of complexity through which managers must navigate to ensure success. Hence, the aim of the research is to apply current knowledge of IOS barriers to emerging automotive cases seeking to achieve responsiveness, and to provide an initial framework for planning such initiatives in the future.

Method

The research examines four recent examples of failed supply chain initiatives that attempt to implement one or more aspects of collaborative planning. The research design reflects the need to deconstruct and understand the reasons why ICT projects were not meeting requirements in the automotive industry. Hence, case selection was based on known automotive initiatives in procurement, supply and operations. The investigation was based on the objective of exploring the barriers governing supply chain integration. This was operationalised through structured interviews and onsite workshops, followed by the development and analysis of a barriers framework (Figure 1). External validity was ensured through obtaining industry feedback on the initial framework and results, gained from multiple data sources (i.e. the OEMs and Suppliers within the initiative). Four supply chain initiatives were selected as examples of collaborative planning that subsequently failed to meet their original objectives. Based on interviews with senior supply chain managers at OEMs and suppliers, the research offers a multi-level framework of barriers to implementing collaborative, inter-organisational systems.

Fig 1 Process Perspective Methodology

Findings

Despite the existence of a comprehensive process model allied with promising results from other sectors, widespread adoption of CPFR has not been forthcoming. This justifies a re-examination of the benefits and barriers. [START_REF] Barratt | Exploring the Experiences of Collaborative Planning Initiatives[END_REF] list the benefits espoused by the growing number of supporters of the CPFR process. Benefits are comprehensively discussed in the literature and would be attractive to any industry seeking clear incentives for implementation. Yet the associated barriers appear to pose considerable complexity for firms that are neither fully understood nor widely disseminated. The cases are now outlined below:

Covisint

Covisint was launched during the dotcom frenzy of early 2000 as the biggest and most powerful automotive internet trade exchange or 'e-hub'. Announced as the beginning of a new era in purchasing and supply chain management, automakers poured $500 million into the Covisint venture that was supposed to transform the automotive industry by using on-line auctions and electronic parts catalogues to drastically reduce material costs. Founder members Ford, General Motors, and DaimlerChrysler (later joined by PeugeotCitroen, Renault and Nissan) anticipated significant price reductions and customer responsiveness by combining purchasing economies of scale and Internet technology. However, rival manufacturers and suppliers were already developing their own solutions and were reluctant to join Covisint over fears of accepting a subordinate role. The vision to create an on-line industry trade exchange for both suppliers and automakers backfired due to issues over anti-trust legislation, agreement over on-line software development, and reluctance from suppliers to subscribe to a system that overnight could slash their component prices. As other trade exchanges proliferated, Covisint's vision of offering collaborative procurement, lower transaction costs, and the introduction of a universal system standard began to diminish (Helper & McDuffie, 2003). In January 2004, the e-hub that was supposed to transform the automotive industry was disassembled and put up for sale (ANE, 2004). It is currently being restructured by an IT provider in the USA to offer on-line services in healthcare.

Everest

Everest was planned as private exchange that would eventually replace all Ford production and non-production purchasing systems around the world, including links to all major suppliers. In 2000, Ford procurement IT consisted mainly of mainframe legacy systems and client databases built up over a long period offering only partial visibility in the US and virtually none across Ford's Premier Automotive Group subsidiaries in Europe: Jaguar, Aston Martin, Land Rover and Volvo. The ambitious nature of Everest presented huge potential for savings where materials spending totalled around $100 billion a year. This meant subsidiaries such as Swedish automaker Volvo was obliged to integrate a large-scale e-procurement hub into its business despite the design being based on US processes and organizational structure that was very different to its own. The scale of Everest created huge difficulties because it required compatibility not only at a technical standards level, but also adherence to common business practices. Thus, the system roll-out encountered difficulties due to internal relations with business units in different regions across the world, and fundamental issues with the supply chain. Suppliers were reluctant to participate in a system whereby buyers could take advantage of increased transparency and reverse auctions. As the original scope of the system began to be scaled back, the project was eventually abandoned.

ICON

The ICON Supply Chain Collaboration (SCC) tool was envisaged as a repository for the OEMs supply requirements, inventory requirements and production capacities of each supplier. The idea was to promote clear visibility between levels of the supply chain, so anticipating production difficulties and allowing optimised stocking. The ICON SCC-Tool algorithms boasted dynamic adjustment of inventory levels for each component to meet the upcoming production needs. Benefits for the supplier included direct access to the demands of the OEM and advice on supply problems via a 'traffic light' system. The tool was implemented by a number of automotive OEMs and their suppliers as an off-theshelf application. However, in order to meet the requirements of all the project partners, information was to be sent either via EDI, e-mail, automatically or manually to an e-mail address. Although a practical method of getting around a real problem in supply chain management, there were difficulties in implementing the full tool (in particular management of capacity information) between automotive OEMs and their suppliers, and was recently discontinued by them.

LiNet

Aimed primarily at the SME sector, LiNet intended to extend and harmonise the supply function by creating seamless supply management systems. LiNet commenced in 2000 as an attempt to capture the requirements of both OEM and suppliers perspectives and derive the requirements for an ideal process. This took the form of a 'Conference Room Pilot' (CRP). The collaborative effort was impressive with project partners including Audi, BMW, DaimlerChrslyer, and VW. The objective was to determine the quantitative and time-referred material requirements for the entire supply network. Demand planning was to be based on customer demands, whose data transferred by EDI. The system was designed to generate order advices for the material planning managers on the basis of primary demands. Capacity planning was to be carried out covertly by the system at each level of the supply network. The CRP was designed to transform diverse formats into a single system making it possible to handle the supply network from every level of the supply chain. LiNet was discontinued in 2003 after legal issues concerning the use of software were found to be insurmountable. Further to this, it became apparent that the standards and rules set up for the use of the Pilot could not have been transferred between customers and suppliers due to inter-organisational culture issues.

Analysis and identification of barriers

An examination of the four failed initiatives: Covisint, eVerest, ICON and LiNet shows a range of barriers that need to be addressed before commencing adoption (Table 1). Industry level barriers are represented by stakeholder dynamics involving power, legitimacy, and urgency, which mediates between the motivations to adopt information systems and the barriers against. Barriers which concern industry as a whole include the issue of resistance to e-hubs or exchanges due to the fear of price cuts, lack of trust in more powerful stakeholder groups, and the increasing burden of network complexity. Further, the competitive conflict between consortium, private, and third party e-hubs to increase membership does not address long-term concerns over adherence to universal industry standards and protocols. Firm level barriers are also represented by the framework. Structural alignment reflects the difficulties for firms to align e-hubs with current organizational hierarchies and the associated problems of interfacing with internal purchasing and supply systems. The cost of e-hub subscriptions and the internal human resource issue rises in proportion to the number of external networks used by the firm. Cultural differences reflect the mismatch in outlook between US and European businesses brought together as an attempt to create closer relations by interacting via e-hubs. This means that suppliers or manufacturers with strong associations to brand or national identity often feel that their independence is threatened by generic, automated links to other organizations. The lack of leadership by senior management is illustrated by a general poor awareness of the potential impact of the Internet and the difficulties over e-hub adoption. User buy-in reflects the fears by people who associate the transparency of ehubs with a perceived threat to individual autonomy, supporting other recent studies (Lippert & Foreman, 2006). Procurement and materials planning and logistics system users are often reluctant to abandon their bespoke IT systems because of the implications of retraining and the uncertainty over long-term benefits from e-hubs and the stability offered by the status quo. 'Accessibility' implies the difficulties of logging on and navigating through password-protected sites, which are often slow, complex, and provide little support for the user. Legacy IT system infrastructure represents a barrier to e-hub adoption because of the difficulties of interfacing bespoke technology to a common business standard across many organizations.

Discussion

This research represents a study of barriers which is unusual as most related research tends to be normative and prescriptive. It adopts a critical view taken from multiple perspectives within the automotive industry, showing that structural, cultural, managerial, user and technical factors need to be overcome if collaborative planning initiatives are to be successful.

Summarising the four cases; Covisint was anticipated to improve transaction efficiency and provide an electronic audit capability. It failed, primarily at the interfirm level where supplier resistance to subscribe to fixed-duration bidding events reduced willingness to collaborate. This was compounded by the inexperience of management to form a robust e-business strategy and ultimately led to resistance in establishing purchasing relationships. Everest was conceived primarily to increase supply chain efficiency and promote cross-functional integration. It failed structurally because of fears of the reduced supplier base it would support and at a user level was perceived to improve transparency a little too far, whilst restricting autonomy. Whilst ICON was envisaged to provide stock level visibility throughout the entire supply chain, it failed culturally on issues of trust allied with the systems inability to manage capacity information. In fact, the level of implementation meant that although it was a useful tool to share inventory information and provide alerts when certain thresholds were met, the strategic possibilities of the tool were not developed. In particular the possibility to monitor supply chain capacities could have provided large benefits in terms of proactively managing future shortages and responding to end customer demand and yet this function was not utilised (for reasons that cut across the barriers levels). Finally LiNet, which espoused to optimise resource planning and supply chain management, was not adopted because of the lack of industry standards combined with managerial hesitation over system and technology selection. A particular problem was the concept of cost and benefit sharing. Supply chain waste can be identified more objectively in a process where there is more visibility of information on inventory and capacity. Thus, agreements have to be made to act to reduce this waste, but this is problematic where multiple partners and sites are involved. In this case agreement was not reached between the project partners. This paper suggests that core issues such as system standardisation must be addressed by industry leaders as a whole, not by individual firms, in order to reduce the burgeoning demands on suppliers to support multiple interfaces. Perhaps an independent organization holds the key to closer integration of automotive information system networks? While government intervention seems a particularly blunt approach, there is at least one example: the 1980s Australian 'Button' programme, where strict central government control over EDI standards ultimately proved beneficial to the industry as a whole. The power of open structures such as the Internet make government or voluntary regulation more difficult in what is effectively an open market and may represent the only viable route for the automotive sector. Already, tier 2 and 3 suppliers are enjoying the benefits of protocols such as XML at a fraction of the cost of vehicle manufacturer and tier 1 supplier bespoke EDI systems. If the issues of Internet governance can be resolved emerging ICT such as web-EDI and collaborative platforms, these tools offer considerable benefits to the industry.

Conclusions

This paper examines the motivations and barriers to collaboration and information sharing across auto industry supply networks. It suggests that firms participating in initiatives which are web-enabled and CPFR-related must give considerable thought towards the barriers to successful collaboration, as well as benefits. The increasing requirement for responsive supply chains (e.g. build-to-order) is clearly evident, where all the cases explored in this investigation offered visions of an integrated and collaborative supply chain. Whilst the approach taken in each case appeared at the outset to be appropriate, during implementation each venture failed to deliver on core objectives. Literature continues to expound the benefits of such systems, where supply integration is largely dependant on electronic tools as enablers to interfirm collaboration. Yet in reality this is an extremely difficult task.

Our framework highlights the significant level of user, managerial, cultural and structural barriers associated with large scale 'big bang' ICT projects. It refocuses attention to the interfirm or industry level barriers, such as supply chain resistance because of lack of resource to support new systems, and the lack of adherence to universal industry ICT standards. In their attempt to emulate more information-intensive sectors such as FMCG, automakers and (to a lesser extent) their partners seem to have lost sight of the core values which make up organisational culture, structure and buyersupplier relationships. The framework therefore presents a practical perspective on ICT implementation for responsive automotive networks, with lessons which may apply to other manufacturing industries struggling to gain profitability in the 21 st century. Future research seeks to explore the issues of regulating information system standards, and the role of trust in e-procurement. In automotive as in other industries, resolving these challenges means the industry as a whole must examines its current processes, whereby the views of all stakeholders (not just the most powerful) are considered in order to achieve greater levels of harmony across the network.

Table 1

 1 Collaborative planning system implementation barriers (Adapted fromHoward et al., 2006)

							Covisint	eVerest	ICON	LiNet
							 Indirect price reduction	 Reduce	inventory	 Stock level visibility	 Optimized resource planning
		Expected benefits				 Direct price reduction  Minimise transactions  Electronic audit capability paper  Transaction efficiency  Control maverick spending	costs  Increase supply chain efficiency  Create more strategic buyers  Cross-functional integration  Opportunity to learn	across the whole supply chain  Capacities across the supply chain	between partners  Management of critical SC situation  Short-term cost saving potentials by reducing re-scheduling efforts, urgent transports, stock levels, etc.  Demand-oriented and functionally
									from Ford	expandable
	Barriers
							Supplier resistance to	Fears that suppliers will	Structural	issues	Lack of industry-wide standardisation
							subscribe	to	reverse	perceive	e-hub	as	(standards) over come	commitment
	Industry	Barriers (Interfirm	level)	delaying launch auctions and VM-owned e-hubs Anti-trust legislation	adoption of 'arms-length' business practices -leading to erosion of trust	through middleware data reformatting	Lack of industry-wide approach to guarantee data security
							New systems must be	Concern over eVEREST	Low cost implementation,	Lack of defined approach for cost-
	Structural	Barriers	(Firm level)	the needs of the centralized purchasing commission Conflict from US & EU firm structures institutionalised to align with	High cost of subscribing to electronic catalogues limited choice of suppliers	service charge not investment, just	benefit sharing
							Cultural mismatch between	Concern over Volvo's	Level of trust endured	Lack of mutual trust between the
							US and European offices	unique	independent	after system switched off	partners with respect to data sharing
		Cultural Barriers		Employees that Ford is still a acknowledge hierarchical-style company	Swedish origins being affected by American process and culture	Did not share capacity information	and data abuse Lack of the culture of creating a win/win situation
							Resistance by managers	Current	processes	Not a strategic process as	Not a strategic process as such, not
							who	already	have	managed by paper and	such, not used to share	used to share capacity information
		Managerial	Barriers		established relationships CEO leadership difficulties purchasing over the management of e-commerce	personality Managers redundancy through the fear supplier e-database	capacity information	Fast development in the ICT sector makes the decision makers doubt whether they are investing in the right ICT technology or not
							Staged implementation of	User fears over the effect	Usually identified before	Variety of user requirements too
							Covisint means it is difficult	of increased transparency	the alert	high
		User Barriers		standard package to identify the system as a definitive product for users Operators used to bespoke system design means difficulties in adapting to a	working by eVEREST restricting the individual's autonomy Worries over people losing their own way of	Can quantify a problem, but reverts to telephone to resolve the problem, as before ICON
							Decommissioning	legacy	Multiple	IT	legacy	Usual website delays	Lack of interoperable systems across
		Technical	Barriers		systems		systems	the SC