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The academic and professional attention to the large synergies hidden in horizontal collaborations is increasing. This study attempts to address the impact of collaborative transport on transportation lead-time and lead-time variability through empirically investigating a group of SMEs involved in a collaborative distribution network. Data was collected for seven pre-cooperation and eight cooperative orders over a period of 14 months. The results of Mann-Whitney U-test show a significant average reduction of 30.8% in the duration of lead times. Lead-time variability was also found to be reduced as the result of changes in the coefficient of variances and the Bartlett's test for homogeneity of variances. Horizontal collaboration in transport could eventually lead to reduced lead times and lower variability of lead time which results in reduced supply chain costs. This can be achieved by means of direct routing and avoiding multi-transshipment routes which reduces the uncertainty and variability by diminishing the number of stages in the transport chain. Thus, effective lead-time management is considered a source of competitive advantage as it can reduce supply chain costs by lowering inventory levels, but is also capable of improving performance and customer service by offering improved product quality service levels.
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I
nt. J. Shipping and Transport Logistics, Vol. 8, No. 1, 2016 51
Copyright © 2016 Inderscience Enterprises Ltd.
Reducing lead-times and lead-time variance in
cooperative distribution networks
Hadi Ghaderi*
Department of Maritime and Logistics Management,
National Centre for Ports and Shipping,
Australian Maritime College,
University of Tasmania,
TAS 7248, Australia
Email: Hadi.Ghaderi@utas.edu.au
*Corresponding author
Wout Dullaert and Walther Ploos van Amstel
Faculty of Economics and Business Administration,
VU University Amsterdam,
De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands
Email: w.e.h.dullaert@vu.nl
Email: w.ploosvanamstel@vu.nl
Abstract: The academic and professional attention to the large synergies
hidden in horizontal collaborations is increasing. This study attempts to address
the impact of collaborative transport on transportation lead-time and lead-time
variability through empirically investigating a group of SMEs involved
in a collaborative distribution network. Data was collected for seven
pre-cooperation and eight cooperative orders over a period of 14 months. The
results of Mann-Whitney U-test show a significant average reduction of 30.8%
in the duration of lead times. Lead-time variability was also found to be
reduced as the result of changes in the coefficient of variances and the
Bartlett’s test for homogeneity of variances. Horizontal collaboration in
transport could eventually lead to reduced lead times and lower variability of
lead time which results in reduced supply chain costs. This can be achieved by
means of direct routing and avoiding multi-transshipment routes which reduces
the uncertainty and variability by diminishing the number of stages in the
transport chain. Thus, effective lead-time management is considered a source
of competitive advantage as it can reduce supply chain costs by lowering
inventory levels, but is also capable of improving performance and customer
service by offering improved product quality service levels.
Keywords: lead time reduction; horizontal collaboration; reliability, logistics
collaboration.
Reference to this paper should be made as follows: Ghaderi, H., Dullaert, W.
and van Amstel, W.P. (2016) ‘Reducing lead-times and lead-time variance in
cooperative distribution networks’, Int. J. Shipping and Transport Logistics,
Vol. 8, No. 1, pp.51–65.
52
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Biographical notes: Hadi Ghaderi was involved as industrial consultant in the
private sectors of food industry before continuing graduate studies. He
graduated with Bachelor of Engineering in Industrial System Engineering from
Shiraz Azad University, Iran and Master of Engineering in Engineering
Management from University Putra Malaysia. He obtained his Masters in
Transport and Maritime Economics from Institute of Transport and Maritime
Management Antwerp (ITMMA), University of Antwerp. He is currently
involved as a researcher, PhD student in rail logistics and casual lecturer at the
Department of Maritime and Logistics Management, National Center for Ports
and Shipping, Australian Maritime College. His research interest is in the areas
lead time management and horizontal collaboration in transport and logistics,
intermodal transport and rail logistics.
Wout Dullaert is Full Professor of Supply Chain Logistics at the VU University
Amsterdam where he heads the logistics department. He is also affiliated with
the Institute of Transport and Maritime Management Antwerp of the University
of Antwerp. He currently lectures in supply chain, production, and operations
management. His research and consulting activities focus on the (re)design of
production and distribution systems, tactical and operational distribution
problems, and cooperation strategies in logistics. He is the Vice Chairman
of the Dutch-Flemish logistics network Vervoerslogistieke Werkdagen
(http://www.vervoerslogistiekewerkdagen.org) and a member of the Board of
the Benelux Association of Transport Economists (http://www.bivec-gibet.org).
He is a member of the editorial board of European Journal of Industrial
Engineering, Journal of Operations and Logistics and the Open Operational
Research Journal.
Walther Ploos van Amstel is Associate Professor at Vrije Universiteit and
Lecturer of City Logistics at Hogeschool van Amsterdam. His research is
focused on sustainable logistics, urban distribution, development of main ports,
sense and responds logistics, mobility and cross chain control centres. He
received his Doctorate in Economics in 2002 from the Vrije Universiteit in
Amsterdam for his research on logistics organisation. From 2002 to 2009, he
was a Professor in Logistics at the Netherlands Defence Academy.
1 Introduction
According to Mason et al. (2007), management in the transport sector occurs along
traditional lines rather than following the contemporary integrated approach. There is
however, evidence supporting a move towards more partnerships and horizontal
cooperation (Schmoltzi and Wallenburg, 2010). In fact, during the last two decades
collaboration within the supply chain management has received a great deal of attention
(Barratt, 2004). Horizontal partnerships have gained momentum among practitioners and
academic researchers over the last five years. Logistics services, operating the physical
link between different levels in supply chains, have a strong impact on the overall
performance of a supply chain and provide numerous opportunities for collaboration.
Reducing lead-times and lead-time variance 53
In particular, lead-time and inventory management play a crucial role in companies’
overall supply chain efficiency and responsiveness. Effective lead-time management is
considered a source of competitive advantage as it can reduce supply chain costs by
lowering inventory levels, but also by improving performance and customer service by
offering improved product quality service levels (Jayaram et al., 1999; Wedel and
Lumsden, 1995; Fransoo and Wouters, 2000).
The focus of this paper is to empirically examine the impact of cooperative transport
on the lead time and/or lead-time variability in a distribution network.
Despite existing research into the financial and strategic advantages of cooperation
this study is – to the best of our knowledge – the first to examine evidence on lead time
and lead-time variability in horizontal collaboration in transport. In addition, this study
provides insights on the conditions for managing and/or reducing lead time in
collaborative transport networks.
The remainder of this paper is as follows. Section 2 provides a focused literature
review on collaboration trends in transport and logistics industry. The methodology to
examine the impact of horizontal cooperation for a real-life case is described in Section 3.
Section 4 provides the computational results and in Section 5 an extensive analysis with
the managerial implications is presented. In the last section, conclusions are drawn and
directions for further research are formulated.
2 Literature review
The development of complex and globally-operated supply chains in an increasingly
competitive environment has encouraged logistics service providers (LSPs) to cooperate
both vertically and horizontally (Mason et al., 2007). During the last decade, horizontal
collaboration has been increasingly developed and is becoming a recognised
organisational form in the logistics and transportation industry. According to Cruijssen
(2006), horizontal collaboration is defined as active collaboration between two or more
firms that operate on the same level of the supply chain and perform a comparable
logistics function on the landside. A recent empirical study in Germany shows that almost
60% of LSPs engage in at least one horizontal partnership (Schmoltzi and Wallenburg,
2010). Horizontal collaboration is a well-known practice in maritime (Benacchio et al.,
2007; Panayides and Wiedmer, 2011; Shepperd and Seidman, 2001) and aviation
industries (Fan et al., 2001; Liou, 2012; Oum et al., 2002; Rhoades and Lush, 1997) in
the form of alliances. Although the literature on horizontal collaboration for landside
operations remains limited compared to the maritime and aviation industry, the body of
research is growing steadily. This literature review focuses on horizontal collaboration in
road haulage and landside operations, covering operational, tactical and strategic levels
of cooperation.
As transport network planning and control can be considered to be the foundation of
horizontal collaboration, it should be carefully defined and implemented. Several papers
have explored operational planning in collaborative networks during the last decade.
Cruijssen and Salomon (2004) perform a simulation study to estimate potential cost
reductions by order sharing between transportation companies between 5 to 15%.
Cruijssen et al. (2007c) discussed the concept of joint route planning in horizontal
collaboration amongst distribution companies. By applying joint route planning,
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economies of density can be attained and distribution costs can be lowered up to 30%.
The allocation of costs and profit sharing for cooperating freight carriers has been
investigated by Krajewska et al. (2008) using the game theoretic solution concept of the
Shapley value. Frisk et al. (2010) examined a case study on transportation of forest
products in Sweden. The authors proposed a cost and saving distribution model based on
equal profit method (EPM). In this model, the potential cost savings depend on the
geographical distribution of the companies in the coalition. Hence, as companies become
aware of the strategic importance of their geographical location, some request a larger
share of the cost savings.
Ghaderi et al. (2012) examine the transportation cost savings of horizontal
collaboration on transport services within purchasing groups. The outcome of their
empirical study shows a reduction of 10 to 30% in outbound logistics cost or tariffs in
comparison to the time period in which the firms were organising procurement by
themselves.
Also from a tactical point of view, horizontal collaboration has received growing
academic research interest. Regarding allocation of costs in collaborative transportation
networks, Ozener and Ergun (2008) developed a model to ensure the sustainability of
collaboration based on cooperative game theory.
Fourth-party logistics management (4PL) was proposed to coordinate horizontal
collaboration between retailers (Hingley et al., 2011). Involvement of LSPs in 4PL
management requires significant investment in IT. This is considered a barrier but
simultaneously offers potential benefits to cooperation. Results from same study shows
major LSPs appear reluctant to get involved in 4PL businesses.
Depending on the possible or desired cooperation level, various characteristic forms
of cooperation were investigated by Leitner et al. (2011), ranging from the joint offering
of transport and logistics services to the coordination of logistics and production
planning. More intense cooperation was shown to lead to higher potentiality in
consolidation, monetary savings and efficiency. The authors consider a neutral
coordination as the guarantee for sustainable success in cooperation between LSPs. Audy
et al. (2011) report on a real-life case of four furniture manufacturers involved in
collaborative transportation. They design a cost allocation structure and perform a
sensitivity analysis to ensure a minimum amount of savings to convince manufacturers to
join the collaboration.
Strategic levels of horizontal collaboration in transportation have also gained
momentum in the last years. Wallenburg and Raue (2011) investigated the role of
governance mechanisms on the conflicts in horizontal collaboration between LSPs. They
showed that relational governance lowers the extent of conflicts in cooperation, while
formal governance leads to a higher conflict level. Furthermore, conflicts decrease the
overall performance of cooperation. Cruijssen et al. (2007a) proposed the first large-scale
empirical study on the potential benefits of horizontal collaboration in logistics and
transportation as well as discussing the main impediments to establishing and
maintaining partnerships in practice. Participants in the survey consider horizontal
collaboration as a motivating option to improve their customer service, but the main
motive was reducing operational costs.
In the first literature review on horizontal collaboration in transport and logistics,
Cruijssen et al. (2007b) identified a lack of a conceptual classification for horizontal
collaboration between LSPs as well as structures for managing and operating cooperation
to be of key importance.
Reducing lead-times and lead-time variance 55
Mason et al. (2007) describe the competitive advantage that transportation companies
can achieve through combining the collaboration of vertical supply chain partners with
the horizontal approach. Combining both vertical and horizontal collaboration provides
innovative solutions for better transport optimisation as well as logistical performance.
Verstrepen et al. (2009) identified the major motives and drivers for LSPs to establish
horizontal collaboration. Secondly, a classification of supporting structure for LSPs
searching for cooperation was proposed. Thirdly, the typical life cycle of partnership and
the management over the time was described. Regarding the typology of horizontal
collaboration among LSPs, Schmoltzi and Wallenburg (2010) define six distinctive types
of cooperation. An additional insight of this study was to show horizontal collaboration
between LSPs is substantially more stable than other partnerships reported by other
scholars (Park and Ungson, 2001; Kale et al., 2002; Hoang and Rothaermel, 2005).
Yilmaz and Savasaneril (2012) studied the coalition formation among small shippers
in a transportation market under uncertainty conditions. The analysis shows that the
shippers always benefit from coalition, but when the benefits are to be allocated, the
coalition may not always guarantee the budget balance, which is fundamental for
sustainability of any coalition.
Over the last few years, the practical relevance and academic challenges have
increased the available literature on horizontal collaboration logistic, especially on the
allocation of costs and on sharing gains In this literature review, we discussed the
available research on horizontal collaboration in transport and logistics by first focusing
on operation, tactical and strategic levels. A large proportion of available literature is
associated with the operational and strategic streams. Within the operational level,
research lies with an empirical-based approach, while the strategic level of cooperation
has been discussed on a theoretical basis. If we are to argue that horizontal collaboration
in transport and logistics offers various opportunities based on the evidences from the
empirical operational cases, and the studies from the strategic level offer a different range
of theoretical solutions, it remains a concern that there is insufficient work on the tactical
aspects to deliver the strategic directions to the operational decision making of
cooperation.
In addition, the focus of the available literature on the potential synergies of
collaboration has been mainly on the direct monetary benefits such as transportation costs
savings. Although reducing the lead-time and lead-time variance could result in lowered
inventory levels and associated costs, in this study we examine this phenomenon from a
time-management perspective and not the supply chain finance viewpoint.
3 Methodology and case study description
The important role of lead-time reduction in supply chain and inventory management has
been widely recognised by practitioners and academic researchers (Leng and Parlar,
2009). Recognising the importance of lead-time management, several papers have been
published, ranging from pure mathematical to qualitative approaches. Although this
amount of research is solid evidence of the importance of control over the lead times,
important research challenges still remain.
To gain control over the lead time, the first step is to identify its constituent
components (Tersine and Hummingbird, 1995). According to Leng and Parlar (2009),
total lead time faced by a retailer from manufacturer consists of three independent
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. Ghaderi et al.
components: setup time (L1), production time (L2), and transportation time (L3);
accordingly,
123
Total lead time L L L=++ (1)
For the purpose of this research, we specifically focus on L3 which is associated with
transportation time. Cruijssen (2006) states horizontal collaboration is often the fastest
way to reduce response times, obtain first mover advantages or successfully enter a new
market. This has been confirmed by practical evidence of cooperating LSPs in Western
Europe (Krajewska et al., 2008; Schmoltzi and Wallenburg, 2010). However, the focus of
previous works is more on the response time than the shipping time in the cooperation.
An example of reduced lead-times made possible by a cooperative logistics concept, is
when carrier companies exchange orders to cut lead times down to levels that would be
impossible to achieve individually (Cruijssen, 2006).
By merging (a part of) the distribution processes of the partnering companies (often
referred to as joint route planning or truck sharing), important efficiency savings can be
obtained for shippers and LSPs involved (Cruijssen et al., 2007a).
In 2010, five small and medium-sized confectionary firms located in Southern Iran
decided to consolidate their shipments from their mutual suppliers in order to minimise
their transportation costs. The targeted materials were not strategic in the nature and were
not subject to seasonal differences in supply. Following Hendrick (1997), this facilitates
cooperation as strategic materials would raise the threats of exchanging strategic
information which can affect the partnership.
Before establishing the cooperation, due to unavailability of direct shipping routes,
the shipments to the confectionary firms were delivered individually from suppliers in the
south, northwest and northeast of the country through two or three consolidation centres.
In the new situation, the shipments are consolidated at the supplier’s plant and are
transported by full truckload (FTL). As such, the cooperation allowed the confectionary
firms to eliminate the need for consolidation during the route at a consolidation centre.
The responsibility of placing, receiving the orders and organising the transportation
service was delegated to a purchasing consortium acting as a third party coordinator. In
this case, we can consider the purchasing consortium to be acting as a 4PL transport
orchestrator. Cooperation in transport of purchased goods by consortia can support
reduced lead-times and higher delivery frequencies or lower the fixed cost of
placing/receiving and order (Ghaderi and Dullaert, 2012).
In this paper, we compare the lead time and lead-time variability during the
cooperative transportation with traditional distribution and address the conditions in
which lead-time reduction can be obtained in a cooperative less-than-truckload (LTL)
transportation network. Because the transportation time accounts for a very large
proportion of the total lead time in this particular case, the handling and logistical times at
grouping/de-grouping points are excluded from the calculations. In addition to the
average lead time, we also want to examine the impact of cooperation on the variability
of lead times. Reducing the variability of lead time results in lower inventory cost by
reduced the safety stock (Simchi-Levi et al., 1999). The collaboration case consists of
three coalitions for firms in which each coalition is associated with one supplier. The
geographical distances and schematic view of suppliers and participating firms are
presented in Table 1 and Figure 1.
Reducing lead-times and lead-time variance 57
Table 1 Distances between players in cooperation (km)
Firm1 Firm2 Firm3 Firm4 Firm5 Supplier1 Supplier2 Supplier3
Firm1 - - - - - 1,628 1,297 797
Firm2 10 - - - - 1,644 1,307 802
Firm3 53 67 - - - - 1,242 -
Firm4 19 16.5 71 - - 1,659 - 791
Firm5 20 17.5 72 1 - 1,660 - 792
Figure 1 Geographical locations of suppliers and firms in region (see online version for colours)
Figure 2 Coalitions and joint route networks
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Based on the geographical position of the firms in the coalition, the closest firm to the
supplier receives the first shipment. However, from the collected data, it was observed
that there are exceptions in the order of the deliveries (e.g. in one order of Coalition1,
Firm2 was visited before Firm1). Figure 2 demonstrates the distribution network between
the firms.
While most of the previous studies on horizontal collaboration in transport and
logistics focus on LSPs, this paper considers shippers managing the transport of
consignments from their mutual suppliers. Quantitative surveys were designed, tested and
the responsible departments were contacted through phone, e-mail and direct interviews.
The surveys focused on quantitative records of transportation lead time, arrival times for
both pre-cooperation and cooperative periods. Data collection period consisted of six
months of pre-cooperation and eight months of cooperative transport, consisting of seven
and eight orders respectively from categorised files stored in both companies and
purchasing consortium. There are some orders procured individually and the lead times
collected here only consist of orders in which all the coalition members are involved.
To examine the impact of horizontal collaboration on transport lead times, the
average lead times for before and after cooperation periods was compared by using of the
non-parametric Mann-Whitney U-test under a significance level of 5%:
0
1
:
:
LTBC LTAC
LTBC LTAC
Hµ µ
Hµ µ
=
> (2)
Lead-time variability plays a crucial role in the safety stock and procurement planning of
industrial firms. To examine the difference between the variances of lead times, we will
use the coefficient of variation (CV) which allows us to compare the relative changes in
variance of lead times for both time periods.
4 Analysis and findings
The first and the main objective of this research focuses on the change in the lead times
experienced by the shippers participating in a horizontal collaboration. Table 2 presents a
summary of lead times for pre-cooperation and cooperative times. For both periods, the
minimum, maximum, mean duration and standard deviation (SD) of the lead times are
reported.
Table 2 Overview of transport lead times in pre-cooperation and cooperative periods (hours)
Coalition Firm
Before collaboration
SD
After collaboration
SD
Min Max Mean Min Max Mean
1 1 51 99 74.8 14.5 42 54 46.7 3.7
2 54 95 74.8 16.5 44 55 48 3.5
4 53 98 76 15.9 45 56 49.5 3.3
5 55 90 72.5 13.6 45 57 50.1 3.7
2 3 74 124 100.2 18.1 43 70 50.5 8.4
1 55 87 72 13 45 72 52.8 8.2
2 63 107 78.4 16.8 46 74 54.1 8.5
Reducing lead-times and lead-time variance 59
Table 2 Overview of transport lead times in pre-cooperation and cooperative periods (hours)
(continued)
Coalition Firm Before collaboration SD After collaboration SD
Min Max Mean Min Max Mean
3 1 28 84 54.7 21.3 26 51 39 9.8
2 31 55 47.8 16.5 27 52 39.2 10.2
4 36 71 58.2 12.1 29 53 40.7 10.1
5 38 57 49.5 9 30 54 41.8 9.9
From the above table it can be observed that the mean lead time has been reduced. This is
confirmed by the result for the Mann-Whitney U-test in Table 3:
Table 3 Mann-Whitney U test for lead time duration
Coalition Firm Z p-value Result
1 1 –3.125 .0005 H0 rejected
2 –3.130 .0005 H0 rejected
4 –3.130 .0005 H0 rejected
5 –3.139 .001 H0 rejected
2 1 –2.898 .001 H0 rejected
2 –2.898 .001 H0 rejected
3 –3.243 .000 H0 rejected
3 1 –1.511 .076 H0 supported
2 –2.435 .007 H0 rejected
4 –1.680 .047 H0 rejected
5 –1.333 .094 H0 supported
Null hypothesis is rejected for all cooperators in first and second coalitions. However,
this hypothesis was only rejected for Firm2 and Firm4 in the third coalition. Although a
lead time reduction is apparent for all firms in this coalition, they are not statistically
meaningful at the 5% significance level for Firm1 and Firm5.
This indicates that horizontal cooperation in transport could reduce the duration of
transportation lead time. By considering all coalition, the lead-time duration has been
reduced on average of 30.79% in this empirical case. Figure 3 illustrates the marginal
changes of means in three coalitions.
The percentage of lead-time reduction or synergy achieved is influenced by the mean
lead time before collaboration, the geographical location of the firm in the joint route
plan and the order of deliveries in the joint route distribution. In this research we have not
separately investigated the different components of transportation lead-time as the line-
haul component and consolidation time. Since the mode of transportation has not been
changed in the cooperative distribution scenario, the average speed of the line-haul
component remained unchanged. However, there are two other causes that result the time
reductions. First, the shorter travelling times which were achieved by means of direct
routes from the supplier to shippers. Second, avoiding the transshipment process
eliminated the unnecessary storage and waiting times in the consolidation centres. The
time savings heavily depend on the coordination level between different parties such as
60
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supplier, transport service providers and shipper in the cooperative network. For example,
removing the line-haul component of the service from supplier to the shipper’s region,
knowing the arrival times enables the shipper to prepare for unloading and consequently
reducing the time a vehicle is spending there. Table 4 shows the percentage of reduction
for coalitions and related shippers.
Figure 3 Changes in average of lead times before and after collaboration (see online version
for colours)
(a)
(b)
Reducing lead-times and lead-time variance 61
Figure 3 Changes in average of lead times before and after collaboration (continued) (see online
version for colours)
(c)
Table 4 Overview of changes in the mean lead time
Coalition Firm % of reduction for firm % of reduction for coalition
1 1 37.56 34.79
2 35.82
4 34.86
5 30.89
2 1 49.6 35.75
2 26.66
3 30.99
3 1 28.70 23.07
2 17.99
4 30.06
5 15.55
It is a well-known fact that the pressure to reduce the cost of inventories in supply chains
has increased. One way to reduce the inventory cost without lowering the service level is
to better manage the lead times and the variability of lead times. Investigating the impact
of cooperative transport on the variability of lead times is the second objective of this
research. For this purpose, we used the CV to analyse the impact of cooperation on the
variability for both time periods. The population under investigation of lead-time
variability analysis consists of three coalitions. The results support reduction in two
62
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coalitions as well as two firms from the third coalition. This indicates that lead-time
variability has been reduced in cooperative transport in 9 cases out of 11.
To examine the variances of lead-times for before and after cooperation, we have
employed the average CV for each firm and Bartlett’s test for homogeneity of variances
(under a significance level of 5%). The results are presented in Table 5:
Table 5 Overview of changes in variances based on CV and the results of Bartlett’s test
Coalition Firm CV before CV after Change% p-value Result
1 1 19.41 8.06 58.4 0.003 Unequal
2 22.06 7.38 66.5 0.000 Unequal
4 21.01 6.74 67.9 0.000 Unequal
5 18.73 7.34 60.8 0.003 Unequal
2 1 18.12 16.63 8.2 0.044 Unequal
2 18.12 15.64 13.7 0.240 Equal
3 21.42 15.78 26.3 0.049 Unequal
3 1 39.05 25.34 35 0.047 Unequal
2 34.66 25.97 25 0.091 Equal
4 20.84 24.70 -18.5 0.340 Equal
5 18.29 23.65 -29.2 0.820 Equal
The results from Bartlett’s test demonstrates homogeneity of variances for all the firms in
Coalition 1, two firms out of three from Coalition 2 and one firm out of four from the
third coalition.
5 Concluding remarks
Highly responsive networks such as fast-moving consumer goods (FMCG) and the
automotive industry illustrate the pressure to better manage inventories. One way to
reduce the inventory cost without lowering the service level is to have a better control
over variability of lead time.
This research aims at examining the lead time elements and potential reductions of
cooperation for shippers and LSPs. This study offers the first empirical analysis on lead
time management in horizontal cooperation in logistics and transport and provides a new
direction for LSPs and industrial firms struggling with managing their transportation lead
times. Cooperation in transport could eventually lead to reduced lead times and lower
variability of lead time which is in general beneficial for reducing inventory costs
(Dullaert and Zamparini, 2013).
We surveyed the literature on lead-time management and examined how cooperation
can affect the lead times of the participating companies for a particular case in the food
industry. We showed that transportation lead time can be reduced by consolidating the
orders which avoids transshipments and multi-drop routes. However, this approach is
suitable when there is not a direct route or in case of infrequent service between supplier
and shipper. Geographical proximity and availability of transportation service between
supplier and shippers plays a crucial role in design and establishment of cooperation. In a
Reducing lead-times and lead-time variance 63
larger geographical zone with less direct routes available, larger economies of scale
(Ghaderi et al., 2012) and more lead time reductions are possible.
Further research will be aimed at developing a decision-making framework for
management of lead time in generic cooperative transport networks by seeing the
coordination costs to control lead time and lead time variability.
Acknowledgements
The authors would like to thank the anonymous reviewers for their valuable comments
and suggestions to improve the presentation of this manuscript.
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... In [38] authors express the impact of collaborative transportation on transportation lead-time through empirically investigating on. In [48] a bi-objective integer programming model is proposed to formulate the problem. ...
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