TOWARDS FOURTH-PARTY LOGISTICS PROVIDERS
A Business Model for Cloud-based Autonomous Logistics
A. Schuldt
1
, K. A. Hribernik
2
, J. D. Gehrke
1
, K.-D. Thoben
2
and O. Herzog
1
1
Centre for Computing and Communication Technologies (TZI), University of Bremen
Am Fallturm 1, D-28359 Bremen, Germany
2
BIBA - Bremer Institut für Produktion und Logistik GmbH, University of Bremen
Hochschulring 20, D-28359 Bremen, Germany
Keywords:
Agents, Cloud computing, Autonomous control in logistics, Internet of things, Fourth-party logistics.
Abstract:
Cloud computing denotes a paradigm shift in computing that enables a flexible allocation of hardware and
software resources on demand. Therewith, it is particularly appealing for applications with a high degree of
computational complexity and dynamics. This paper identifies logistics planning and control as a promising
application for clouds. However, two prerequisites must be met for cloud-based logistics control. Firstly, the
platform-as-a-service layer must provide a synchronisation of the physically distributed real-world material
flowsand the data flows in the cloud. Secondly, appropriate and scalable control software must be implemented
on the software-as-a-service layer. Apart from outlining the technical foundations, this paper describes how
both steps enable a business model that is usually referred to as fourth-party logistics.
1 INTRODUCTION
The cloud computing paradigm envisions that hard-
ware and software resources are flexibly allocated on
demand. Usually, three different layers are distin-
guished (Rittinghouse and Ransome, 2010):
Infrastructure as a Service refers to a scalable
hardware infrastructure.
Platform as a Service covers a system environment
for application deployment in the cloud.
Software as a Service means that users are provided
with the whole software demanded by them.
Employing clouds raises several questions which
cover, for instance, service reliability, connectivity,
and security. Indisputably, these questions are worth
being addressed. Nevertheless, these questions are
out of the scope of this paper which lays its particular
focus on advantages and resulting use cases.
A promise of cloud computing is that users can
significantly decrease their investments for own IT
infrastructure. In principle, only thin clients are re-
quired to access cloud resources through the Internet.
The required computational power is billed based on
the resources actually used. Particularly the ability
to allocate services dynamically distinguishes cloud
computing from application service providing in gen-
eral. Conventional IT infrastructures must be capable
of handling the maximally expected load. Cloud com-
puting approaches this challenge by bringing together
users with complementing demands, i. e., load peaks
of some users coincide with idle times of others (Rit-
tinghouse and Ransome, 2010).
The ability to flexibly adapt to both increasing and
decreasing demands makes cloud computing partic-
ularly appealing for applications that exhibit a high
degree of computational complexity and dynamics.
This particularly holds for logistics planning and con-
trol. As a foundation, characteristics of the logistics
domain are examined and the resulting requirements
are matched to properties of cloud computing (Sec-
tion 2). The first requirement is that the software for
logistics control is scalable. To this end, an agent-
based approach for autonomous control in logistics is
presented (Section 3). The second requirement is that
the physically distributed logistics processes are con-
nected to the control system. The necessary synchro-
nisation of material and data flow is accomplished
with Internet of Things technology (Section 4). Com-
bining both aspects with cloud computing allows im-
plementing a business model which is often referred
to as fourth-party logistics, 4PL in short (Section 5).
445
Schuldt A., A. Hribernik K., D. Gehrke J., Thoben K. and Herzog O..
TOWARDS FOURTH-PARTY LOGISTICS PROVIDERS - A Business Model for Cloud-based Autonomous Logistics.
DOI: 10.5220/0003392104450451
In Proceedings of the 1st International Conference on Cloud Computing and Services Science (CLOSER-2011), pages 445-451
ISBN: 978-989-8425-52-2
Copyright
c
2011 SCITEPRESS (Science and Technology Publications, Lda.)
The contribution of this paper is thus a new busi-
ness model for logistics control based on cloud com-
puting. The foundation of this business model is the
integration of agent-based autonomous control in lo-
gistics and cloud computing by means of Internet of
Things technology.
2 CHALLENGES OF LOGISTICS
PLANNING AND CONTROL
Logistics is of considerable importance as a backbone
of the globalised economy. Goods are procured from
all over the world, processed, and distributed to cus-
tomers which can again be located all over the world.
Managing such supply networks is a challenging task
due to three properties:
Complexity which is due to the high number of lo-
gistics objects and their manifold parameters.
Dynamics induced, e.g., by transient customer de-
mands and changes in the environment.
Distribution as logistics processes frequently span
companies, countries, and even continents.
Computing optimal plans that incorporate all relevant
aspects takes a considerable amount of time (Apple-
gate et al., 2007). Due to the dynamics, optimal plans
are therefore often already outdated in the moment
their generation is finished (Windt and Hülsmann,
2007). This is particularly challenging for logistics
control which requires quick responses to exceptions
that occur while plans are executed.
Given that logistics planning and control can be
executed in parallel, cloud computing is a promis-
ing approach to tackle the challenges caused by com-
plexity and dynamics with increased computational
power. The problem, however, is that optimisation
problems in logistics are usually solved with dis-
crete linear programming which is known to be NP-
complete (Hopcroft and Ullman, 1979). Although
the artificial Travelling Salesman Problem, as an ex-
ample, might appear simple at first glance, it never-
theless exhibits a factorial computational complexity.
Even with sophisticated heuristics, the seemingly in-
exhaustible hardware power of clouds can often not
suffice for solving such problems in acceptable time
(Applegate et al., 2007).
This means that an adequate Software-as-a-
Service layer is required that is capable of reducing
the computational effort and coping with the local
dynamics occurring. Given this prerequisite (which
will be addressed in Section 3), it is possible to bene-
fit from the scalable Infrastructure-as-a-Service layer
of clouds. This is particularly beneficial in order to
tackle global dynamics occurring. These dynamics
may be due to economical or seasonal influences as
well as special sales concepts. Examples for the for-
mer ones are the financial crisis of 2007–2010 as well
as Christmas sales. An example for the latter one are
sales concepts with a continuously changing range of
products (Schuldt, 2010). With cloud computing, lo-
gistics companies would be able to scale their compu-
tational power for process control in accordance with
these effects.
Unlike many other cloud applications, however,
logistics control exhibits another challenge. Logis-
tics control demands frequent interaction with the real
world. On the one hand, this means that the soft-
ware system must be informed about events occur-
ring in the real world. On the other hand, the sys-
tem must be able to react on these changes, i. e., its
decisions must be executed in the real world. The
required synchronisation of material and data flows
is particularly challenging because logistics processes
are inherently distributed. This high degree of spa-
tial distribution prevents local information from be-
ing available for central decision-making (Jedermann
and Lang, 2008). This is part of the Platform-as-
a-Service layer (which is dealt with in Section 4),
which connects the Infrastructure-as-a-Service and
the Software-as-a-Service layers.
To summarise the findings so far, the requirements
for cloud-based logistics control are as follows:
1. On the Software-as-a-Service layer, it is necessary
to implement an adequate control software that
is capable of coping with both the computational
complexity and dynamics.
2. On the Platform-as-a-Service layer, it is necessary
to synchronise real-world material flows with the
data flows in the cloud to account for the physical
distribution of logistics processes.
3 AGENT-BASED AUTONOMOUS
CONTROL IN LOGISTICS
As elaborated in the previous section, conventional
approaches to logistics planning are not applicable
for on-line control. Due to their asymptotic compu-
tational complexity, even the computational power of
clouds does not suffice (Section 2). A particular chal-
lenge is the dynamics of logistics processes that ren-
ders optimal plans outdated in the moment their gen-
eration is finished. Think, for instance, of a shipping
container loaded with perishable goods. The shelf
life of the goods decreases if the interior tempera-
ture increases. If a sensor network within the con-
CLOSER 2011 - International Conference on Cloud Computing and Services Science
446
tainer detects such an increase in temperature, it is
thus necessary to re-route the container to another lo-
cation nearby and to send another container with sim-
ilar goods to the original destination. Other reasons
for re-planning include traffic or weather conditions.
All these reasons for dynamics have in common that
they only affect some logistics objects and by far not
the whole network.
Motivated by this finding, the paradigm of au-
tonomous control in logistics delegates decision-
making to the participating logistics objects them-
selves (Windt and Hülsmann, 2007). Autonomous
control enables logistics objects to process informa-
tion, to make and execute decisions, and to cooperate
with each other based on objectives imposed by their
owners. The advantages over centralistic approaches
are as follows (Schuldt, 2010):
1. The computational effort is significantly reduced
by computational decomposition.
2. The scalability of process control is significantly
increased by parallelising decision-making.
3. Reactivity and robustness are significantly in-
creased by local exception handling.
Potential autonomous logistics entities are compo-
nents, articles, sales units, cardboard boxes, pallets,
and shipping containers.
The technologies enabling autonomous logistics
are identification, localisation, sensors, communi-
cation, as well as local data processing. Fig-
ure 1 depicts an architecture for autonomous lo-
gistics entities (Schuldt, 2010). The identification
unit uniquely identifies autonomous logistics entities
(Hribernik et al., 2009). The localisation unit al-
lows self-localisation of logistics entities, e. g., based
on global navigation satellite systems (Hofmann-
Wellenhof et al., 2008). The sensor unit continuously
monitors the environmental and the interior state of
the logistics object (Al-Karaki and Kamal, 2004; Jed-
ermann and Lang, 2008). The communication unit
enables coordination with other entities. The heart
of autonomous logistics entities, however, is the data
processing unit which checks whether the original
planning for the object is still valid or whether it
must be updated. Its decision-making can be imple-
mented by means of intelligent software agents. Ex-
ample applications of agent-based autonomous logis-
tics cover adaptive truck routing incorporatingdriving
time estimation based on traffic and weather condi-
tions (Gehrke and Wojtusiak, 2008) and autonomous
container dispatch (Schuldt, 2010).
A physically distributed application of software
agents is possible (Adorni et al., 2001). For instance,
this makes sense in order to continuously monitor
Identification
Unit
Localisation
Unit
Sensor
Unit
Data Processing Unit
Communication Unit
Autonomous Logistics Entity
Other Participant
Autonomous
Logistics Entity
Environment
Figure 1: Architecture for autonomous logistics entities.
sensor measurements without the necessity to trans-
mit them over communication networks. Embedded
systems attached to the logistics objects, however, are
not designed for extensive reasoning tasks. Instead,
clouds are a promising platform for such re-planning.
Agent-based implementations natively support
parallel execution. In contrast to sequentially exe-
cuted software solutions for logistics control, soft-
ware agents are thus well prepared for virtualisa-
tion. Multiagent platforms such as JADE (Bellifem-
ine et al., 2007) support multiple so-called agent con-
tainers in which agents can be deployed at differ-
ent locations. Agents can then communicate with
each other also over the boundaries of their particu-
lar agent container. Interoperability can be ensured
because the agent concept has been shown to be
sufficiently close to the service-oriented architecture
(SOA) model (Moreau, 2002). A number of con-
cepts exist which interface multiagent platforms with
SOA middleware, such as WS2JADE (Nguyen and
Kowalczyk, 2007), middle agents (Sycara, 2001), and
other gateway architectures which allow transparent,
fully automatic interoperation (Greenwood and Cal-
isti, 2004).
In order for cloud-based autonomous logistics to
be operationalised, however, an adequate approach
must be developed to synchronise the material and
data flows in the respective logistics processes. The
actual logistics objects whether they be physical
such as goods, containers, trucks and transport hubs
or immaterial such as orders need to be connected
both to the agents which are their digital counterparts
in the cloud and to existing logistics IT infrastruc-
ture such as dispatch, route planning, and enterprise
resource planning systems in order for cloud-based
logistics service provision to be feasible. In terms
TOWARDS FOURTH-PARTY LOGISTICS PROVIDERS - A Business Model for Cloud-based Autonomous Logistics
447
of the architecture for autonomous logistics entities
(Figure 1), this means that the data processing unit
which is then located in the cloud must be connected
to all other units.
4 MATERIAL AND DATA FLOW
SYNCHRONISATION
To summarise the findings thus far, agent-based au-
tonomous logistics helps cope with the computa-
tional complexity and dynamics on the Software-as-a-
Service layer (Section 3). Another important prereq-
uisite (Section 2), however, is the synchronisation of
material and data flows on the Platform-as-a-Service
layer. In particular, three challenges have to be ad-
dressed:
Unique identification of logistics objects to estab-
lish a link between real world objects and their
agent counterparts.
Data integration from varioussources in a meaning-
ful manner.
Dynamic data source integration such as RFID,
sensors, sensor networks, and other systems in-
tegrated in logistics objects.
For these purposes, the concept of the Internet of
Things becomes relevant. In essence, the Internet of
Things extrapolates the idea of the Internet a global,
interconnected network of computers to describe
a network of interconnected things, such as every-
day objects, products, and environments. As such,
the concept represents the convergence of a num-
ber of recent multi-disciplinary developments such as
Ambient Intelligence (Ducatel et al., 2001), Ubiqui-
tous (Weiser, 1991) and Pervasive Computing (Gupta
et al., 2001), Auto Identification (Cole and Engels,
2002), and Intelligent Products (Meyer et al., 2009).
At the heart of the concept lies the idea that objects
things are capable of information processing and
communication with each other and with their envi-
ronment. For cloud-based logistics control, the logis-
tics objects are the things which the multiagent sys-
tem enables to process information and communicate.
An important prerequisite for autonomous logis-
tics based on cloud computing is its integration into
existing logistics infrastructures. Therefore, it is im-
portant to synchronise real-world material flows and
data flows in the cloud. This mapping can be accom-
plished based on the identification standards of the
EPCglobal Architecture Framework (Hribernik et al.,
2009). ID@URI using the Dialog system (Främling
et al., 2006) is an alternative that combines unique
article identifiers with Internet addresses where addi-
tional information about the object can be retrieved.
Furthermore, it is necessary to integrate data from
various sources in a semantically meaningful manner.
To this end, semantic mediators can be applied. Fig-
ure 2 illustrates a concept for a generic data integra-
tion concept for logistics clouds with the Internet of
Things. It is an extension of a concept for generic
data integration in autonomous logistics (Hribernik
et al., 2010) which satisfies requirements both to-
wards the coupling of the material and data flows as
well as towards providing unified access to all rele-
vant data sources including interfacing to the service
consumers. At the heart of the concept lies a medi-
ator component (Ullman, 1997; Wache et al., 2001),
which is capable of composing queries to any combi-
nation of relevant logistics data sources. It achieves
this by semantic mediation. Each data source is fully
described syntactically and semantically by an ontol-
ogy, which can be mapped onto the others by the me-
diator. Wrapper components handle the transforma-
tion to and from the relevant data sources in a rule-
based fashion. By implementing semantic descrip-
tions and transformation rules for widely used lo-
gistics data exchange formats such as the EDIFACT
subset EANCOM or standards from the EPCglobal
Architecture Framework such as EPCIS (Electronic
Product Code Information Services) (EPCglobal Inc.,
2007), access to the majority of relevant data sources
is given. Additional, proprietary data sources can
be integrated simply by adding a new wrapper with
the relevant semantic description and set of transfor-
mation rules, making the concept highly extensible.
This approach also allows the service consumer to ei-
ther easily integrate the required services into its own
logistics IT landscape, or, for example, utilise thin
clients to access a web-based GUI towards the cloud
services.
Finally, the proposed concept also facilitates the
direct integration of dynamic data sources used in lo-
gistics processes, such as such as RFID, sensors, sen-
sor networks and other systems integrated into phys-
ical logistics objects. By abstracting from the physi-
cal interfaces towards these data sources, the seman-
tic mediation approach may be applied in much the
same way it is to static data sources. The abstraction
layer is required to be able to provide a reliable in-
terface regardless of the physical accessibility of the
dynamic data sources at any time. It is responsible
for buffering, filtering and routing data to and from
the respective data sources. It may consist of ele-
ments such as the FOSSTRAK HAL towards EPC-
compliant RFID (Floerkemeier et al., 2007), PMI
(Promise Messaging Interface) (Främling and Ny-
CLOSER 2011 - International Conference on Cloud Computing and Services Science
448
Enterprise Systems
(ERP, WMS, TMS, etc.)
EPCglobal Architecture Framework
(EPCIS, ONS, etc.)
EDI and Messaging
(EDIFACT EANCOM etc.)
Proprietary
Interfaces
Multiagent System of
Autonomous Logistics Entities
Data Processing Unit
RFID Sensors
Hardware Abstraction Layer
Semantic Descriptions and Transformation Rules
Ontology-Based Mediator
Generic Integration Services
Embedded Systems
Semantic Data Integration
Logistics Data Sources
Standard Logistics Interfaces
Data Processing UnitData Processing Unit
Autonomous Logistics Entity Autonomous Logistics Entity Autonomous Logistics Entity
Figure 2: Data integration concept for cloud-based autonomous logistics entities.
man, 2008) towards PEIDs (Product Embedded In-
formation Devices) (Kiritsis et al., 2003) or OSGi to-
wards sensor components (Ahn et al., 2006).
5 BUSINESS MODEL FOR
FOURTH-PARTY LOGISTICS
Despite all of the advantages cloud computing offers,
the broad variety of involved technologies makes it
challenging for individual logistics companies to ben-
efit from these advantages. Logistics enterprises are
experts in logistics and not in IT. Employing spe-
cialised service providers that offer logistics cloud
computing is thus preferable. This leads to different
business models for service providers.
5.1 Logistics Cloud Service Providers
The first three layers of logistics cloud computing
correspond more or less directly to the three general
cloud layers Infrastructure, Platform, and Software as
a Service (Section 1). Nevertheless, service providers
for logistics clouds have to ensure the services out-
lined in Sections 3 and 4. However, it is worth men-
tioning that service providers on different layers may
be disjoint from each other. That is, providers on
higher levels may acquire services on lower levels
from other service providers.
On the Infrastructure-as-a-Service layer, there is a
direct correspondence between logistics cloud com-
puting and general cloud computing as both refer
to the underlying hardware infrastructure. Conse-
quently, service providers on this level do not need
any expertise in logistics. On the contrary, it seems
even advantageous if logistics companies share com-
puting resources with enterprises from completely
different branches, at least given that their computa-
tional demands complement each other. This helps
utilising the resources of the infrastructure service
provider more efficiently.
Logistics-specific tasks on the Platform-as-a-
Service layer can be categorised as follows. Firstly,
fundamental services for agent deployment in the
cloud have to be provided (Section 3). This eases the
deployment of agent representatives and delegates the
administration of the software platform to the service
provider. Secondly, the synchronisation of material
and data flows has to be implemented in order to es-
tablish the link between real-world logistics objects
and their digital counterparts in the cloud (Section 4).
For data integration, they provide interfaces to data
sources and mediate if necessary. Logistics compa-
nies do thus not have to consider these issues them-
selves and can thus implement their logistics control
without being burdened with data integration.
The Software-as-a-Service layer of logistics
clouds goes even one step further. Service providers
offer a complete implementation of the software
agents needed for autonomous control of a specific
process. Furthermore, the administration of software
agents is left to the service provider. Consequently,
service providers on this layers need expertise in the
field of logistics control with multiagent systems. In
return, the need for IT expertise in the logistics com-
pany is reduced to a minimum because its task re-
duces to delivering relevant process information.
5.2 Fourth-party Logistics Providers
On the Software-as-a-Service layer, the cloud
provider offers the software to control logistics pro-
TOWARDS FOURTH-PARTY LOGISTICS PROVIDERS - A Business Model for Cloud-based Autonomous Logistics
449
cesses. The Platform-as-a-Service layer provides the
means to interconnect the IT systems with the real-
world logistics objects. Logistics enterprises are
thus relieved from concerning the IT infrastructure.
Instead, their task reduces to ensuring that the re-
quired logistics resources and information are avail-
able. Apart from the cloud infrastructure, enterprises
thus have to make framework contracts with logistics
service providers, e. g., about transport and storage re-
sources. These are then available to the agent repre-
sentatives of logistics objects in the cloud. For in-
stance, an agent can initiate a transport for a shipping
container it represents based on the pre-negotiated
framework contract.
The service of the first three layers can even be
improved by also providing direct access to logis-
tics resources, i. e., the pre-negotiated framework con-
tracts for logistics resources become superfluous. To
this end, the cloud service provider additionally offers
an electronic marketplace on which logistics service
providers and consumers can negotiate (Smith, 1977)
on logistics services:
1. An agent wants to allocate logistics resources for
its respective logistics object.
2. Matching logistics service providers are discov-
ered by means of semantic service descriptions.
3. A negotiation between the service consumer agent
and the agents of the potential service providers
takes place.
4. The best offer is chosen.
This means that also the procurement of logistics ser-
vices is moved into the cloud. Logistics customers
thus get a ready-to-use solution for integrated logis-
tics control. In this business model, it is not neces-
sary to bill customers for the utilisation of cloud re-
sources explicitly. Each transaction in the cloud is di-
rectly linked with a transaction of logistics resources.
Billing for cloud services can thus be directly cou-
pled with billing for the logistics services. The costs
for computing will usually be negligible compared to
the negotiated logistics services like transport.
A cloud service provider acting on this level might
become what is often referred to as a fourth-party lo-
gistics provider, 4PL in short. Fourth-party logistics
providers are considered to have no own logistics re-
sources. Instead, they employ sophisticated IT sys-
tems in order to integrate services from freight opera-
tors and stockists, forwarding agencies, and outsourc-
ing companies in the field of contract logistics.
6 CONCLUSIONS AND
OUTLOOK
Logistics control is a promising field of application
for cloud computing. Logistics processes exhibit both
a high degree of complexityand dynamics. Therefore,
a scalable IT infrastructure is required for supply net-
work management. The computational complexity of
conventional approaches to logistics planning makes
them inapplicable for on-line control. By contrast,
the paradigm in agent-based autonomous control in
logistics can serve as the Software-as-a-Service layer
of logistics clouds. In this approach, decision-making
is delegated to agent representatives of the individual
logistics objects. For this purpose, the synchronisa-
tion of material flows in highly distributed logistics
processes and their corresponding data flows in the
cloud is another challenge. It can be accomplished
with Internet-of-Things technology which is imple-
mented on the Platform-as-a-Service layer.
Providing each of the above services is a business
model on its own. Both relieve logistics enterprises
from IT-related tasks, thus enabling them to focus on
their core business. From their combination, however,
emerges another promising business model: fourth-
party logistics providers who do not have own logis-
tics resources but combine services of other providers
to custom-tailored logistics.
The investigation so far shows that cloud-based
autonomous control actually implies promising busi-
ness models (Section 5). The feasibility of imple-
menting the Platform-as-a-Service (Section 4) and
the Software-as-a-Service (Section 3) layer has been
shown in this paper. The next step following this fea-
sibility study is thus to actually implement cloud com-
puting for logistics control.
ACKNOWLEDGEMENTS
This research is funded by the German Research
Foundation (DFG) within the Collaborative Research
Centre 637 Autonomous Cooperating Logistic Pro-
cesses: A Paradigm Shift and its Limitations”
(SFB 637) at the University of Bremen, Germany.
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