European Journal of Scientific Research
ISSN 1450-216X Vol.44 No.3 (2010), pp.420-429
? EuroJournals Publishing, Inc. 2010
https://www.doczj.com/doc/9815852544.html,/ejsr.htm
Analysis of Transportation Cost in Overall Logistics Cost Management of Manufacturing Companies in
Southwestern, Nigeria
Somuyiwa, Adebambo Olayinka
Dept of Transport Management, Ladoke Akintola University of Technology
P.M.B 4000, Ogbomoso, Oyo State, Nigeria
E-mail: miyagi89@https://www.doczj.com/doc/9815852544.html,
Tel: +2348033570837
Abstract
This paper evaluates the importance of transportation cost in overall logistics cost management, with a view to establishing the role of transport in the supply chain, while
focusing on inherent costs incurred in transport cost, and how cost reduction can be
established, simultaneously maintaining the correct levels of customer service. Various
Transportation costs components-Labour cost; Owned vehicle cost; Rented vehicle cost
and others were identified and analysed using SPSS and adapted distribution function of
Cobb-Douglas that incorporates Ordinary Least Square and Weighted Least Square
methods. All these components were regressed on the Volume/Quantity of goods
transported. Twenty (20) manufacturing companies formed the sample of the study, based
on multi stage sampling techniques that incorporated cluster, stratified and purposive
sampling methods. Apart from parametric test statistical techniques adopted, data analyses
were done using a software application that incorporated Cobb-Douglas production
function, which was packaged and tailor-made for the paper. It was revealed that there
were significant relationships among components of transportation cost. The paper
recommends that emphasis should be placed on number of owned vehicles, labour cost,
rented vehicle cost and distance over which goods are carried so that cost reduction can be
achieved, simultaneously maintaining customers’ service.
Keywords: Analysis, Costs, Logistics, Transportation, Management,
1. Introduction
The significant roles of transport in physical distribution of materials are well appreciated and need not be over stressed, rather, what is pertinent is how this factor, and other major actors of logistical operational activities will be harnessed towards enhancement of effective supply chain management. Suffice it to acknowledge the fact that, there is a positive and linear relationship between these two concepts (logistics and supply chain) (Somuyiwa, 2010). Transport cost proves to be major area where unnecessary supply chain cost can be incurred. Inefficient load planning, often leads to longer turn around, that will subsequently lead to the cost escalation of the transport operation and may also have an impact on the customer service levels. The effect of lower inventory levels can escalate the cost associated with transport due to smaller and more irregular loads. According to Cooke (2000) ineffective routing and scheduling of orders or the absence thereof can also impact on transport cost.
Analysis of Transportation Cost in Overall Logistics Cost Management of
Manufacturing Companies in Southwestern, Nigeria 421 In production process, ineffective and inefficient transport and perhaps warehouse management are detrimental in obtaining a seamless and optimized supply chain. Consequently, the role of inventory management in both areas should (transport and warehousing) not be overlooked. It is also important that trade-offs between transport, inventory and warehousing will have to be made to ensure that the cost and service optimization of the supply chain is achieved.
Similarly, according to Franceshin and Rafele (2000) total logistics cost analysis has been proven to be the key to managing the logistics functions, consequently, it is important that management considers the total of all logistics costs. Controls are expected to be instituted to minimize the total costs of logistics rather than to minimize the cost of each component. However, determining which component of logistics costs to reduce can be problematic, since there is a trade-off between costs components. Infact, attempts to reduce the cost of individual logistics activities in isolation may even lead to greater Total logistics costs. It is in this regard that at the level of the firm, attempts are often be made to integrate outbound logistics system so as to holistically minimize total logistics costs within the context of Translog cost function (Somuyiwa, 2010).
Again, achieving logistics cost reduction by organizations is often approached by supply chain optimization that involves planning to ensure that the demands are met and supply limitations minimized to provide an acceptable level of customer service. For instance, Giblin (2001) observed that transportation is the greatest single cost category in logistics and it is the best to book for dramatic cost saving. Also, Skjott-Larsen (2000) argued that the priority has not only been to reduce transportation costs, but also to increase customer service levels. Thus, in order to meet the ever-increasing expectations that are placed on the transportation function, “the basic work of transportation has changed from operationally meeting low cost or high service criteria to providing a strategic edge by simultaneously meeting elevated service requirements and increasingly lower costs” (Stank and Goldsbug, 2000). Hence, it can be deduced that the strategic edge of optimized transportation function is linked to two factors: obtaining the lowest possible distribution cost while meeting the desired customer service level.
Furthermore, considerable works have been done in the area of transportation in supply chain management; with all tend towards transportation cost reduction, without consideration on the overall total cost and profitability of the companies. All these have lead to the introduction of strategic alliances, such as third and fourth party logistics (Milligan, 2000; Lambert, 2001; Factor, 2001; Somuyiwa and Sangosanya, 2007 and Somuyiwa, 2010).
However, Braddy (2000) opined that significant supply chain cost reductions from a transportation perspective can be obtained, but that one needs to look further than network optimization and rate reductions with carriers. He further asserted that these methods involve network, lane and node decision-making that form an integral part of transport management solution which include transport planning, vehicle routing and scheduling, delivery execution and shipment tracking and performance management. However, these have not been able to examine relative importance of transportation cost within the context of customer’s satisfaction and competitive advantage.
In the light of this, the paper evaluates the importance of transportation cost in overall logistics cost management, with a view to establishing the role of transport in the supply chain, while focusing on inherent costs incurred in transport cost, and how cost reduction can be established, while maintaining the correct levels of customer service. In this section, various Transportation costs components were identified and analysed using SPSS and adapted distribution function of Cobb-Douglas that incorporates Ordinary Least Square and Weighted Least Square methods. Similarly, hypothesis of insignificant relationship among components of Transportation cost was tested. All these components were regressed on the Volume/Quantity of goods transported, so that attention will be focused on the most relevant ones in attempt to reduce cost.
422 Somuyiwa, Adebambo Olayinka 2. Methodology/Study Area
2.1. Study Area
South-Western part of Nigeria lies between latitude 60N and 8?0N of the equator and longitude 30E and 50E of Greenwich Meridian Time (GMT).The zone consists of Six States. These are Lagos State that stretches along the seaboard, Ogun, Oyo, Osun, Ondo and Ekiti State. The South-Western Geo-political Zone occupies an area of 79,048 Square Kilometres. The Zone covers about one-twelfth of Nigeria, and into it are packed almost 25 million or about one-fifth of the entire population of the Country. The area is washed in the South by the Gulf of Guinea. On the east it is bounded by South-Eastern Nigeria. On the West, it shares a common frontier with the Republic of Benin; and on the north, it is bounded by North Central Geo-Political Zone that consists of Kwara State, Kogi State, Niger State and others. The majority of the people in South-Western Nigeria are Yorubas, which occupies major urban centres of this Geo-political Zone
In a related development, major population concentration are found in the state capitals and other important towns in the region like Ikorodu, Epe and Badagry (Lagos state) ; Abeokuta, Ijebu ode, Ijebu Igbo, Shagamu, Ilaro, Ifo, Otta, and Aiyetoro (Ogun state); Ogbomoso, Iseyin, Oyo, Ibadan, Kishi, Igboho,and others (Oyo states). Other towns include Iwo, Gbongan, Ikire, Ifon, Ede, Ikirun, Ilesha and Oshogbo (Osun state); Owo, Ikare, Akure, Ondo, Okitipupa and Oka Akoko (Ondo state) and Ise Ekiti, Efon, Alaye and Ado Ekiti in Ekiti state.
There have been considerable increase in the population figures of these states; for instance, Oyo state was estimated to be 3.5 millions in 1991 and 5 millions in 2005. Lagos was estimated to be 10 million in 2005, while Ogun state was estimated to be 3.5 million in 2005 population census (NPC, 2006). It is interesting to note that all these can be attributed to the economic activities, which targentially determine the rate of the distribution of these products.
2.2. Methodology
Data set for this paper was sought from Twenty (20) manufacturing companies that are within the ambit of Food, Beverage and Tobacco sectoral group, between the years of 2002 and 2006. The choice of this particular manufacturing group is predicated on its ubiquitous nature of these companies in the study area. Again, their products directly affect people’s life such that they have socio-cultural implication, especially their rate of consumption. Above all, the sectoral group is one of the most quoted sectors at the stock market; consequently, accessibility to information about it was not problematic.
Sequel to the above, model and equations were developed for the paper through Cobb-Douglas production function that is related to inbound logistics, but now adopted to outbound logistics, as presented thus
TRc = Weight of goods + Number of vehicle owned + Number of Deliveries + Labour cost + Owned vehicle cost + Rented vehicle cost + Distance at which goods are carried Equation:
Tc =α+β1(C) +β2(K) +β3(S) +β4(L) +β5(O) +β6(R) +β7(D) + e 1 Where α= constant
Tc = Vol./Quantity of goods transported
C = Weight of goods
K = Number of vehicle used
S = Number of Deliveries
L = Labour cost
O = Owned vehicle cost
R = Rented vehicle cost
D = Distance over which the goods are carried
Analysis of Transportation Cost in Overall Logistics Cost Management of
Manufacturing Companies in Southwestern, Nigeria 423 β1,β 2,β3,β4,β5,β6,β7 are the associated output elasticities and e represents the error term. Also
for estimation purposes, the above function was linearized by taking logarithms of equation (1) and
adding an error term.
This is done by using a system of five equations, one for each year:
Log(Q01) =01+1Log(C01)+2Log(K01)+3Log(S01)+4Log(L01)+5Log(O01)+6Log(R01)+7Log(D01)+01 2
Log(Q02)=02+1Log(C02)+2Log(K02)+3Log(S02)+4Log(L02)+5Log(O02)+6Log(R02) +7Log(D02)+02 3 Log(Q03)=03+ 1Log(C03)+2Log(K03)+3Log(S03)+4Log(L03)+5Log(O03)+6Log(R03)+7Log(D03)+034
Log(Q04)=04+1Log(C04)+2Log(K 04)+3Log(S04) 4Log(L 04)+5Log(O04)+6Log(R04)+7Log(D04)+04 5
Log(Q05)=05+1Log(C05)+2Log(K05)+3Log(S05)+4Log(L05)+5Log(O05)+6Log(R 05)+4Log(D05)+05 6
Where Q, C, K, S, L, O, R &D were defined in equation (1)
This model was used to determine the relationships among components of transportation cost in
overall logistics management.
3. Literature and Conceptual Issues
Authors have argued that distribution network is influenced by measurement of the following:
Response time, product variety, product availability, customer experience, order visibility and returnability. Implicit therefore, a network designer needs to consider product characteristics (innovative and functional), as well as, network requirements when deciding on the appropriate
delivery network (Chopra, 2003). Hence, the network is tailored to match the characteristics of the
product or the needs of the customers. For instance, fast and emergency items are stocked locally or
have them directly shipped while the slower moving items are stocked at distribution centres. Again,
very slow moving items are typically drop-shipped from the manufacturer and involve in a large lead
time. (Chopra, 2003 and Crainic, 2002).
Indeed, network design models are extensively used to represent a wide range of planning and
operation management issues in transportation, logistics and production distribution. This according to
Crainic (2002) is more pronounced in Logistics structure and service network and operations of a long
distance freight transport systems. The author further stressed that network design is particularly
relevant to firms and organizations that operate consolidated transportation systems and typically
related to the planning of operations. Perhaps this is often referred to as strategic / tactical or tactical /
operational according to the planning traditions and horizons of the firm.
Chopra and Meindl (2001) affirmed that the ultimate aim is to respond to demand and ensure
the profitability of the firm, consequently, one of the major objectives of tactical planning is to achieve
the best trade-off between operating costs and firm profitability and service performance measured
(cost efficiency and responsiveness), such that total logistics cost will be reduced and firms revenue
and profit could be enhanced.
3.2. Avenues for Transport cost Reduction
The traditional methods used by shippers to counter inevitable increases in transportation costs are competitive bidding, optimization of networks, redesign of networks and a repetition of the previous
steps (Brandy, 2002). These methods involve network, lane and node decision –making that are not
within the ambit of this paper.
Similarly, Brandy (2002) is of the opinion that great saving can be made in the area of critical
logistics planning of outbound transportation, inventory management, production planning, warehousing distribution and purchasing are needed to reduce static inventory.
In a related development, Transport management information systems should be integrated with
external enterprise system that will assist to achieve a more optimized supply chain, as well as, lower
transport and total logistics/ supply chain costs. In other words, company that is more effective in
transport function will also be more efficient in providing better customer service.
424 Somuyiwa, Adebambo Olayinka 3.3. Transport and Customer Service
Customer service is described as ‘fuel that drives the logistics supply chain engine’ (Coyle et al., 1996). The core principle is to have the right product to the right customer in the correct quantity, without any damage. However, at the end of the 1980s the focus was shifted from cost reduction to improvements in customer service. Improving on supply chain performances led to improvements in revenue growth and higher profitability through the gaining of market share. During this time the focus of reducing supply chain assets and total costs in the supply chain slowed remarkably (Davis and Drumm, 1996). Transport and the efficiency thereof will have a major impact on both cost reduction and customer service, both from an outbound and inbound distribution point of view (Coyle et al., 1996)
3.4. Elements of Customer Service from an Outbound Distribution Point of View
The following important elements of customer service with regard to outbound distribution will be discussed: order cycle time, delivery time and reliability.
3.4.1. Order Cycle Time
From the sellers’ point of view, the time dimension will be order cycle time but from the buyers’ point of view it will be lead time or replenishment time (Coyle et al., 1996). Order cycle time is the total time from the placement of the order at the supplier until the customer receives the goods (Lambert et al., 1998) and the lead time for the supplier is from the time that the order is placed with the manufacturer until the goods are received.
3.4.2. Delivery Time
This is the time from the receipt of the order until the customer has received the goods (Gilmour, 1997). Measuring and controlling the time from the parliament of the order until the customer receives the goods may be difficult if the seller uses contact carriers as he no longer has any direct control over the goods(Coyle et al., 1996)
3.4.3. Reliability
Reliability can be described as keeping promising delivery schedules. Any variation in the delivery schedule must be communicated to the customer (Gilmour, 1997). For some customers, reliability or dependability is more important than lead time. If lead time is consistent, the customer can keep lower levels of inventory and the costs of possible stock-outs can be discarded (Gilmour, 1997) An important element of reliability is to ensure the safe delivery of the product or consignment. If goods are damaged or lost, the customer cannot use them and will face possible losses in sales due to stock-outs. The customer will therefore need to keep extra or excess inventory, which will lead to higher costs in terms of inventory holding. A damaged shipment can lead to a claim from the customer, which will result in added transport costs for the seller to send a replacement (Coyle et al., 1996).
These in turn have indirect link with distribution channel management, which has undoubtedly become very complex. The basic customer service offering are not adequate anymore. Competitors have duplicated the service offerings and can now offer the similar or the same services. As a result of this, customers have become used to excellent service and will continue to expect even better service in the future. The increase in anticipated customer service has led to matching increases in transportation cost.(Fawcett, Mcleish and Ogden, 1992)
3.5. Cost drivers in Transport Activities
In view of the above, it is pertinent to highlight some examples of cost drivers in the transport activity centre, with a view to giving an idea about what cost driver could be, relatively to pricing in transportation business.
Examples of cost drivers related to transportation activities are:
Analysis of Transportation Cost in Overall Logistics Cost Management of
Manufacturing Companies in Southwestern, Nigeria 425 ?Volume of goods carried
?Weight of goods carried
?Distance over which the goods are carried
?Number of deliveries
?Labour hours
4. Analysis and Discussion
Regarding the transportation cost analysis; this paper used the sum total of average volume /quantity of goods transported for each of the five years (2002-2006) as surrogates for Transportation cost. The reason for the choice of five years had been elaborated upon at methodology section and need not be repeated. However, the main results were obtained through a weighted 2-step least squares estimation of the system of equations.
On the other hand, the independent variables (input measures) average weight of goods, average distance over which the goods are carried, average number of deliveries, average number of vehicles used, labour cost, owned vehicle cost and rented vehicle cost. Pearson Product Moment Correlation Coefficient (PPMCC) was used to determine the relationship between and among these variables through SPSS, and consequently used to test the hypothesis.
4.1. Transportation Cost Parameters
This study modeled manufacturing companies as operating according to the Cobb-Douglas production function.
Data set used for analysis therein, was based on average of data on transport components of these companies from 2002 – 2006. Hence, the average of these years under study (2002 – 2006) is presented in Table 2, while the variables definition is presented in Table 1
Table 1: Variables label and Definitions
Variables Descriptions
VOD Volume of Goods Distributed
WOG Weight of Goods
NOH Number of Owned Vehicles
NOD Number of Deliveries
LAB Labour Cost
OVC Owned Vehicle Cost
RVC Rented Vehicle Cost
DGC Distance Over which the Goods are carried.
Source: field survey (2009)
426
Somuyiwa, Adebambo Olayinka Table 2: Average of Transport components records Millions N’(2002-2006)
COYS VOD WOG NOH NOD LAB OVC RVC DGC
ForeDaiPlc
68 .07 .0001 .007 3.8 117 00 .06 LiveFedPlc
167 5.6 .0003 .010 5.2 276.2 1 1 OktOilPlc
94.2 .3 .00008 .004 4.6 116.8 00 .07 GuinesPlc
32.6 .3 .0006 .03 8.2 00 732.2 1.2 IntBrewPlc
4.9 .2 .0002 .006 4.1 188.5 1.1 .08 NigBrewPlc
39.7 .3 .0006 .3 6.8 00 634 1.2 NigBottPlc
91.3 .3 .0004 .5 8.4 527 00 1.1 ConBrewPlc
5 .2 .0003 .00
6 5 174.8 .98 1 7upPlc
75.4 .2 .0003 .03 4.7 284 00 .1 NasacoPlc
90 4.3 .0002 .006 4.8 112.4 1.23 .05 UniDisPlc
55.4 7.4 .0002 .004 4.7 102.2 1.4 .05 DanSugPlc
81.1 5.6 .0004 .007 5.8 184.3 00 .07 BigTretPlc
92 .06 .0001 .007 4.8 87.6 .07 ,06 TateIndPlc
95.7 .06 .0002 .007 4.8 117 .08 .06 CadburyPlc
221.8 .3 .0003 .03 7.5 00 335 .2 NestlePlc
303.8 .1 .0003 .3 8.2 00 376 .2 UTCNigPlc
32.1 .06 .0001 .05 4.4 110 00 .04 WAMCO Plc
74.6 .07 .0001 .1 3.9 72.6 00 .04 DanFloPlc
181.6 51.2 .0003 .8 7.6 306 00 .07 FlourMilPlc
164.2 5.8 .0004 .07 7.4 216 1.2 .06 Source: fieldsurvey (2009)
Based on this table a programme was written with Visual Basic .All in line with Cobb-Douglas
cost function as elaborated in the previous section. In a similar vein, the results of correlation coefficients using Pearson Product Moment Correlation Coefficient (PPMCC) through SPSS is presented in Table 3
Table 3: Zero-order Correlation Coefficients between the Dependent and Independent Variables of Transport
Components
VOD WOG NOH NOD LAB OVC RVC DGC
VOD
1.000 .307 .624* .412 -.505* -0.237 .451 -.515* WOG
1.000 .346 .477 .133 .314 -.287 -.082 NOH
1.000 .623* .747** .321 .712** .669** NOD
1.000 .597** .403 .321 .521* LAB
1.000 .312 .572* .430 OVC
1.000 -578* .511* RVC
1.000 .564* DGC
1.000 ** Correlation is significant at the 0.01 level
* Correlation is significant at the 0.05 level
Source: Output of SPSS Analysis, Based on fieldsurvey, 2009
One general observation in Table 3 is that, there is moderately high positive correlation, but
low negative inter-correlation among the dependent and independent variables. This perhaps makes the data sets not to violate the assumption of Multi-collinearity in the multiple regression model; because of correlation value of less than +0.8 as a rule of thumb and as suggested by Oyesiku (1995).
Another important thing that is noticed in the table is that three (3) of the independent variables
are significant at 0.05 level of significance, relatively to dependent variable. These are NOH (.624); LAB (-.505) and DGC (-.515). The implication of these negatives is that the more the volume of the goods distributed (VOD), the less the labour cost (LAB) and owned vehicle cost (OVC).Indeed, this is
Analysis of Transportation Cost in Overall Logistics Cost Management of
Manufacturing Companies in Southwestern, Nigeria 427
in line with economies of scale that can be achieved if transportation parameter is well managed in outbound logistics. On the other hand, the relative significance of all these variables relatively to dependent variable, accentuates the importance of VOD as a surrogate for transport cost. Similarly, there are inter-correlation among these independent variables.For instance, Number of owned vehicles (NOH) is correlated significantly with other variables such as Number of deliveries (NOD (.623), Labour cost (LAB (.747), Rented Vehicle cost (RVC (.712) and Distance over which goods are carried (DGC (.699). In the same token, NOD has significant relationship with LAB (.599) and DGC (.521), while Owned vehicle cost (OVC) has significant inverse relationship with Rented Vehicle cost (RVC(-.578), but a direct relationship with DGC (.511), RVC is only correlated with DGC (.564). It is worthwhile to stress that the inverse relationship between OVC and RVC only buttresses the argument that, the more the Owned Vehicle Cost (OVC), the less the Rented vehicle cost (RVC).
Furthermore, most obvious among all these inter and intracollinearity (correlation) is that they are all significant at 0.01 and/or 0.05 level of significant. This confirms the reliability, adequacy are accuracy of the data sets. In a related development, ordinary least square scheme of the multiple regression model that is incorporated in the software that was developed for the analysis of the data set reveals in Table 4 that multiple R is 0.829, while the R 2 is 0.6872. This confirms that the level of explanation of these entire variables to the volume of goods distributed is 68.7% and the F-value is
2.225 that is significant at 0.01 and 0.05 level of significant (see table 5).
Table 4: Multiple Regression Generation Model for Components of Transport CostDependent VOD (Volume
of Good) Independents Variables
b Coefficient Standard Error of b
Beta weight T-value Sig WOG
48.391 13.012 .239 2.269* 0.017 NOH
37.242 9.124 .347 2.107** 0.000 NOD
46.279 11.014 .135 2.568* 0.021 LAB
51.578 14.350 .562 3.378* 0.007 OVC
-7.309 2.320 -.625 -3.150** 0.000 RVC
-11.437 3.385 -.918 -2.644* 0.003 DGC
-15.308 1.211 -.566 -3.124** 0.001 CONSTANT
75.416 23.122 3.618** 0.000 VOD =75.416 + 48.391 (WOG) + 37.242(NOH) + 46.279 (NOD) + 51.578 (LAB) -7.309 (OVC) -11.437 (RVC) -15.308
(DGC) + e
* Significant at 0.01 level of significance
** Significant at 0.05 level of significance
Multiple R = .829
R 2 = .6872 or 68.7%
Adjusted R 2 = 0.6184
F-values = 2.225**
Source: Output results of Cobb-Douglas function based on field survey 2009
Table 5: Analysis of Variance
Sum of Square Df Mean Square F value P-value
Regression 63282.330 7 9040.319 2.225 0.000
Residual 48750.850 12 4062.571
Total 112033.18 19 Source: Output results of Cobb-Douglas function based on field survey 2009
Moreover, based on Table 4 most of T-values of these independent variables are significant at 0.05 level of significance or 95% confidence interval. Suffice it to acknowledge that all these
428 Somuyiwa, Adebambo Olayinka independents variables (transport components) did not only account substantially to Volume of goods distributed (VOD), but also significant. It goes to confirm that they are adequate and accurate to model volume of good distributed, which is a surrogate for Transport cost. Again, it is important to recall that significant inter correlation were established among components of Transport cost, consequently upon this, the hypothesis that stated that there is no significant relationship among components of transport cost is rejected and alternate hypothesis is aceepted, that there is significant relationship among components of transport cost. Indeed it is obvious, when a cursory look is taken at the relationship between/and among these variables, for instance, one should expect a relationship among volume of goods distributed (VOD) and number of vehicles owned (NOH) labour cost (LAB) and Distance on which the goods are carried (DGC). Similarly, there was considerable level of relationship among, number of vehicles owned (NOH) and Rented vehicle cost (RVC) as well as DGC. Perhaps of importance to all these variables, is the fact that substantial proportion of all these variables have relationship with distance of which the goods are carried (DGC)
Sequel to all these, it is interesting to note that, if anything is to be done regarding to customer satisfaction and cost reduction, emphasis should be placed on NOH, LAB, RVC and DGC, such that it will not only enhance profitability, but as well accelerate competitive advantage that logistics is known for.
Conclusion
The efficient of outbound logistics is critical to the whole logistics, because if products are not effectively and timely distributed, it will lead to spill-over and friction that the result will be confusion. Sequel to this, customer should be able ton order a product and rest assured that it will be delivered on time. In other words, transport, if not well managed can have major negative impact on the total logistics supply chain costs, as well as customer service. Hence, in order to prevent these, companies and logistics service providers must understand some inherent cost of these cost drivers. For instance, the study recommended that, emphasis should be placed on Number of owned vehicle; Labour cost; Rented vehicle cost and Distance over which goods are carried in the area of cost reduction. This can be achieved by contracting, Transportation activity centre to logistics service providers, who in turn, will group distribution, through vehicle scheduling and routing, that will usher in economies of scale and ultimately cost reduction.
There have been extensive changes in the transportation/distribution arena in the last decade. Transport has proven to be one of the largest cost elements in the supply chain and can add value to the effective supply chain functioning from both cost and customer service perspective. The efficiency of the transport function can have a major impact on customer service. It is in the light of this, and against one of the cardinal objectives of this paper: to evaluate the relative importance of transporation cost in overall logistics cost, with a view to determining the significant relationship among these components of logistics cost. The results, however, revealed inter-significant relationship among these components, and equally accounted substantially to the overall supply chain management.
In view of these, it is pertinent for the transport managers in conjunction with distribution managers to understand the inherent and importance of components of Transportation cost as pointed out from our analysis, especially (Number of owned vehicles, Labour cost, Rented vehicle cost and Distance over goods are carried), so that adequate planning can be done in line with vehicle routing and scheduling for effective transportation cost optimization, to ensure customer satisfaction and at lower transport cost.
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