=Paper=
{{Paper
|id=Vol-1848/CAiSE2017_Forum_Paper2
|storemode=property
|title=Smart Logistics: An Enterprise Architecture Perspective
|pdfUrl=https://ceur-ws.org/Vol-1848/CAiSE2017_Forum_Paper2.pdf
|volume=Vol-1848
|authors=Prince M. Singh,Marten van Sinderen,Roel Wieringa
|dblpUrl=https://dblp.org/rec/conf/caise/SinghSW17
}}
==Smart Logistics: An Enterprise Architecture Perspective==
https://ceur-ws.org/Vol-1848/CAiSE2017_Forum_Paper2.pdf
Smart logistics: An enterprise architecture
perspective
P.M. Singh, M.J van Sinderen, and R.J Wieringa
Services, Cybersecurity and Safety
University Of Twente,
Enschede, The Netherlands.
(p.m.singh, m.j.vansinderen, r.j.wieringa)@utwente.nl
Abstract. Logistic enterprises are increasingly becoming smarter and
more efficient by using real-time contextual data. A currently unsolved
problem for small to medium sized logistic service providers (SMLSPs) is,
how to use real time data in existing business processes & IT systems.
Enterprise architecture can be used as a tool to solve this problem and
aid in adapting existing processes & IT. This would lead to improved
operational planning and disruption handling; thereby bringing them
closer to becoming smart, context aware logistic enterprises.
Keywords: smart logistics, enterprise architecture, context awareness,
smart planning, disruption handling, operational planning
1 Introduction
In this era of interconnected businesses and systems, enterprises have access to a
large amount of potentially useful contextual data. Using this data, enterprises
can provide smart products/services, leading to more profits. As observed in the
case of DHL [1][2] data from diverse sources is analyzed to turn disruption in
the supply chain into competitive advantage. Nevertheless, SMLSPs find it chal-
lenging to incorporate contextual data in existing processes. In existing logistics
research, this aspect has not yet been adequately investigated[3]. Examples of
frequent challenges faced by SMLSPs are (a) what activities must be performed
for smarter services? (b) what will be the changes to existing process? (c) does
the enterprise have required IT capability? (d) how can different data source be
integrated? Furthermore, SMLSPs are frequently unaware of the latest techno-
logical trends and state-of-the-art technologies available for smart services and
business process improvements. For example, big data analytics can improve de-
mand prediction and negotiations with partners LSPs (logistic service providers),
but, very few SMLSPs are doing it[2][4]. We choose to focus on SMEs because:
– they have limited resources but have to compete with big players in the
market. In order to compete, they must move towards smart logistics.
– they often don’t have an enterprise architecture (EA) which makes it difficult
to achieve business-IT alignment.
X. Franch, J. Ralyté, R. Matulevičius, C. Salinesi, and R. Wieringa (Eds.):
CAiSE 2017 Forum and Doctoral Consortium Papers, pp. 9-16, 2017.
Copyright 2017 for this paper by its authors. Copying permitted for private and academic purposes.
– existing literature rarely derives smart logistic requirements from the per-
spective of SMLSPs.
Activities for smart logistics are motivated by industry requirements which we
gathered via interviews with representative SMLSPs 1,2 . EA (enterprise architec-
ture) modeling is used to illustrate how these activities would fit in the current
business/IT landscape of a SMLSP. It is well known that logistic companies can
improve transport planning, track & trace services and order closure by using
contextual real time data[5][6][7][8][9][10].
The main contribution of the paper is to motivate and present a generic EA for
smart logistics scenarios. The primary target audience are SMLSPs which can
use this EA to adapt their processes and IT systems. This will facilitate their
transition to smart enterprises.
Our research consortium consists of logistic companies including Seacon1 , CTT2 ,
CAPEGroep3 and OVSoftware4 . This research is part of the Synchromodal-IT
project supported by Dinalog5 .
2 Background
The term smart logistics is frequently used in logistics literature, yet, there is
not a widely agreed definition[11]. Although, this may not necessarily hinder the
use of latest technologies by SMLSPs, it does create hindrance for researchers
to build upon and combine similar existing works. Moreover, as observed in [3],
most literature on smart logistics focuses on RFID technology but the use of
other state-of-the-art technologies is usually ignored. In this paper we choose to
motivate smart logistics processes from the industry point of view. We conducted
interviews with SMLSPs to inquire what, according to them are smart logistics
processes?
GS1 Logistics Interoperability Model (LIM)[12] and One Common Frame-
work for Information and Communication Systems in Transport and Logistics
(OCFTL)[13][14] provide an overview of the top level business processes in any
logistics company. On one hand, LIM is a representative reference model for lo-
gistic companies, while on the other hand, OCFTL incorporates the results from
8 European projects over logistics. A mapping between LIM and OCFTL can be
found in [14]. As mentioned in [12], these top level business processes are 1) Inter
operation Agreement 2) Master Data alignment 3) Logistic services conditions 4)
Long term planning 5) Operational Planning 6) Execution and 7) Completion.
In this paper we focus on Operational Planning and Execution, because these
are the core processes for SMLSPs. The sub-processes for Operational Planning
and Execution are shown in Fig. 1.
1
www.seaconlogistic.com
2
www.ctt-twente.nl
3
www.capegroep.nl
4
www.ovsoftware.nl
5
www.dinalog.nl
10
For smart logistic, enterprises have to use data from diverse sources. This data
can be static (e.g. order data), near-real time (e.g. data from hubs, terminals) or
real time (e.g. AIS data)[9]. Furthermore, data can also be in different formats
i.e. unstructured (e.g. emails), semi-structured (e.g. XML) or structured (e.g.
from databases). The integration of all this data into existing processes presents
a big challenge for companies and is frequently ignored by existing researches
[15]. Furthermore, a road map towards smart logistic services is not found in
literature. This paper tries to fill this research gap by using EA to show the use
of contextual data and state-of-the-art technologies in operational planning and
execution.
Fig. 1: Main business processes in logistics
3 Methodology
We followed design science research methodology [16] for this research. Following
are the steps of our method.
1. Problem Investigation. Six interviews were conducted with six SMLSPs
to identify desired improvements in operational planning and disruption han-
dling. As additional information the interviews also provided insights about
the current business and IT infrastructure of the companies. Using this in-
formation, an as-is EA of a generic SMLSP was made. [17]. Due to space
constraints this is not included in this paper.
2. Treatment Design. Based on interview results, a to-be EA of a generic
smart SMLSP (Fig. 3) was made. This EA includes desired process im-
provements and relevant state-of-the art technologies. It give a holistic view
(business level, application level, infrastructure level) of a smart SMLSP’s
planning and execution process.
3. Treatment Validation. As a preliminary validation of the to-be EA, a
web application is being developed using Mendix[18]. Once developed, LSPs
would be able to use the web application in conjunction with their exist-
ing applications to achieve smarter planning and execution. To validate the
usefulness and utility of the web app. another series of interviews will be
the conducted. We have devised a 3-step validation experiment, which is
explained in Section 5.
11
4 Enterprise Architecture in a smart logistic context
For designing the to-be EA there can be two possible approaches:
1. Top-down. Start with the desired improvements/requirements as indicated
by SMLSPs, during interviews and then choose applications and data that
are necessary for these improvements.
2. Bottom-up. Start with available (or new) data sources, state-of-the art tech-
nologies (applications) and use then to implement the desired improvements.
We chose to adopt a top-down approach owing to its relative ease w.r.t treatment
validation. We have chosen ArchiMate as the EA modeling language to model the
to-be EA because of its wide spread acceptance in the EA modeling domain and
it being an OMG standard[19]. The following subsections discusses the desired
process improvements by SMLSPs.
4.1 Operational planning
1. Use of constraints. Based on the order characteristics, the planner should
be able to choose which data sources to taken into account while planning
a transport route. E.g. exclude weather information (alerts) but include live
traffic (or traffic predictions) while planning an order route. Moreover, dif-
ferent routes should be prioritized such as, least CO2 emitting route, fastest
route, shortest route and cheapest route.
2. A Dashboard. Interviews with LSPs indicated that a dashboard [20] in-
creases the efficiency of planners. Among the main requirements, for such a
dashboard, was the possibility to see events that can cause future disrup-
tions, live trace & trace of shipments and expected future orders. Such a
dashboard enables a good overview and control over operations.
3. Smart planning module. SMLSPs often do manual operational planning.
Smart planning implies a lot of different functions[10]. The recurring fea-
tures of a smart planning module during the interviews were: (a) demand
aggregation, (b) optimization of load for every transport trip (c) optimum
route selection (d) use of demand patterns while planning (e) use of real
time contextual information and (f) re-planning in case of disruptions.
4. Integration of new data sources. SMLSPs want to get data from new
data sources which provide real time contextual information. By combining
data from these sources they can make a well informed prediction of the
current (and future) situations and plan accordingly. E.g. AIS data from
ship routes to predict ETA (estimated time of arrival) of deep sea vessels [7].
5. Use of a common data model Data from diverse sources raises syntactic
and semantic interoperability challenges. The use of a common data model
[9] helps in combining data from different sources.
6. Big data analytics. The use of big data analytics in improving logistic ser-
vice is already done by big players in logistic [1]. Yet its is quite difficult for
logistic SMEs to use those technologies due to the investments it requires.
Another reason is it difficult for them to gauge the ROI on big data ana-
12
lytics. Big data analytics can help in predicting demand patterns, expected
disruptions etc. thereby improving the enterprise’s agility.
4.2 Execution
1. Track and Trace. The most important activity during (transport) execu-
tion is locating a shipment, in logistic terms, ETA determination. Monitor-
ing of shipment and determining ETA is a cumbersome task [9]. Currently,
planners have to collect data from different websites for this. Techniques like
web-scrapping should be integrated should be included in a dashboard for
easy monitoring ETA estimation.
2. Disruption Handling. In most SMLSPs, disruption handling is an ad-
hoc process. The planner has to continuously monitor various websites and
inquire infrastructure providers for any current (e.g. accidents) or future
disruptions (e.g planned maintenance of rail roads). Furthermore, in case of
disruptions re-routing of shipments is seldom done (Fig. 2). In [21] a detailed
study on disruption handling options for a SMLSP was done. The to-be EA
Fig. 2: Current disruption handling process
has a disruption handling module, whose main functions are (a) to collect
data from pre-defined sources (e.g. websites/APIs/sensors etc.) (b) check
which shipments are (or can be) disrupted (3) inform the planner as an alert
(on the planner’s dashboard) in case an action is required and (4) allow
re-planning of a shipment. This way, the planner doesn’t have to manually
check for disruption, instead web-service and APIs are used to collect this
information.
5 Discussion
This paper highlights the use of state-of-the-art technologies and contextual data
to achieve smarter operational planning and execution by SMLSPs. A definition
of smart logistics is purposely not provided, rather the concept is motivated via
interviews with LSPs. Thus, this paper aims to extract smart logistics require-
ments from SMLSPs and propose an architectural approach to address these
requirements based on state-of-the-art technologies. Big logistic companies like
DHL, already have a robust IT infrastructure in place and use state-of-the-art
technologies. SMLSPs usually face a challenge in this respect.
13
14
Fig. 3: Representative To-be enterprise architecture for a smart LSP
Their resources are limited. Therefore, they should identify their goals, pri-
oritize them, and relate them to relevant business processes. This will ensure
maximum ROI on new technological investments. The to-be EA (Fig 3.) can act
as a tool for SMLSPs to compare and contrast their own EA (or to create one).
Using it they can devise a step-by-step road map towards smarter operational
planning and execution. A major challenge for SMLSPs, which is not discussed
in this paper, is, access to logistics physical infrastructure (hubs, inter-modal
terminals, etc.). The lack of infrastructure hampers activities like re-routing and
modality switch when a shipment is en-route. Therefore, either SMLSPs should
make agreements with infrastructure service providers or develop their own to
fully implement smart execution.
Validation of the proposed to-be EA is in process and consists of the following
3 steps. These steps are conditional and sequential:
1. The components of EA are developed, e.g., a common data model [9], a
smart planning algorithm [5][6] and a planner’s dashboard [22].
2. These components will then be integrated (as shown in to-be EA) in the cur-
rent processes of a set of selected SMLSPs, who are prepared to participate
in a validation research experiment.
3. Step 3 is interviews with selected SMLSPs. The aim of these interviews will
be to gain answers to the questions, (a) do the proposed new components
play an instrumental role in achieving smart logistic goals? (b) what addi-
tions and improvements are required in the proposed EA.
6 Conclusion
Enterprises must adapt to changing business environment and market demands,
to stay in business. In this paper we have shown how SMLSPs can solve basic
challenges towards implementing smart service. We can conclude that (a) smart
logistics consists of a number of activities that contribute towards desired goals
(b) these activities can be mapped to an EA that in turn facilitates a gap analysis.
(c) an EA allows enterprise to devise a step-wise plan towards smarter logistic
services. The authors do not claim that the list of requirements obtained via
interviews, is an all-inclusive list of features of smart logistics. Such as exercise
must be repeated with other stakeholders to develop a consolidated and well
grounded list of requirements. Also, to avoid tunnel vision and localization of
results further validation of the to-be EA model for smart logistics is required.
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