Int. Journal of Business Science and Applied Management, Volume 11, Issue 2, 2016

Do freight transport time savings translate to benefit for

transport consuming companies?

Evangelos Sambracos

Department of Economics, University of Piraeus

80, Karaoli & Dimitriou Str

Piraeus, 18534, Greece

Tel: 0030 2104142283

Email: sambra@unipi.gr

Irene Ramfou

Faculty of Business and Economics, AKMI Metropolitan College

74, Sorou str, Maroussi, Athens, 15125, Greece.

Tel: 0030 210 6199891

Email: iramfou@metropolitan.edu.gr

Abstract

It is common practice in Benefit - Cost analysis to consider freight transport time savings (FTTS) as a

benefit for both transport producing and consuming companies. While transportation projects and

policies resulting in FTTS are expected to have a positive effect on carriers’ performance reducing time

related transport costs and improving service, this is not always the case for the demand side of the

transport market. Using System Dynamics in order to model the internal supply chain of a transport

using company and simulate several scenarios, we argue that FTTS do not necessarily translate to

benefit for shippers, but their effect depends strongly on the structure of the company’s decision

making process.

Keywords: Freight transport time, benefit – cost analysis, Systems Dynamics

Int. Journal of Business Science and Applied Management / Business-and-Management.org

1 INTRODUCTION

According to the latest EU’s Guide to Cost-Benefit Analysis of Investment Projects travel time

saving is one of the most significant benefits that can arise from the construction of new, or

improvement of, existing transport infrastructure (EU, 2015). FTTS are expected to have a positive

effect on carriers’ performance reducing time related transport costs such as driver wage costs and

vehicle operating costs per trip as well as improving service, especially reliability, facilitating the

timely delivery of transported goods. However, the mechanisms that link FTTS to supply chain

benefits and business performance are much more complex (US DOT, 2006, Sambracos and Ramfou,

2013, 2014).

Traditional Cost-Benefit analysis does not fully account for the benefits of transport improvements

that accrue to shippers from cost savings and service improvements since it mostly considers first order

benefits (US DOT FHWA, 2004). Several studies exist that try to fully quantify the benefit that freight

transport companies can realize from FTTS, however there is no consensus nor on the magnitude of

this effect not on the methods used to elicit it demand (Feo-Valero et al. 2011).

In this paper Systems Dynamics modeling and simulation is used in order to explore the

mechanisms that translate FTTS to benefits for transport consuming companies. After a review of prior

research in the field, a generic model is built and several scenarios are developed in order to measure

the value of FTTS.

2 PRIOR RESEARCH

The value of freight transport time savings (VFTTS) is the benefit that derives from a unit

reduction in the amount of time necessary to move a shipment from an origin to a specific destination.

Demand for freight transport is a derived one, resulting from the spatial interaction between complex

business processes. Therefore, in order to understand the value of savings in freight transport time, it is

necessary to consider the wider context of logistics, production and trade activities, through which time

acts as a resource (Tavasszy and Bruzelius, 2005).

Traditional CBA focuses on first order benefits from FTTS that include reduced vehicle operating

times and reduced costs through optimal routing and fleet configuration for the carriers. Transit times

may affect shipper costs also such as for spoilage and also scheduling costs. In the short run, demand

for transportation is rather inelastic since nothing changes for the shippers except for the cost of freight

movement, since they continue to ship the same volume of goods the same distance between the same

points (US DOT FHWA, 2001).

Longer term reorganization gains due to FTTS refer to adjustments that transport consumers

(shippers and consignees) make in their logistical arrangements in response to lower costs of freight

movement. Tavasszy (2008) classified firm’s responses to FTTS into three categories that include

transport, inventory and production reorganization. The first, involves changes in routes, type of

vehicle used, modes of transport. Time influences the amount of inventory in transit and the value of

the finished good. The second, involves the number, location and volume of inventories with time

determining which clients can be served by which warehouse within service level targets. Finally,

production reorganization involves a shift between materials used, changes in production location or

basic production technology changes. It is evident that FTTS benefits have a dynamic character since

they evolve over time and do not strictly coincide with the time of the transport project. Producing a

time table of benefits realization could only be indicative since the time that elapses between the FTTS,

the reaction of the firms to it and the materialization of the benefit (or loss) varies. Such benefits are

very difficult to be monetized and used in CBA but are expected to be 15% above direct user benefits

(US DOT, 2004).

Reorganization effects are firm specific according to Boston Logistics Group who provided rough

“first-cut” estimates (based on surveys on firms) of such benefits from a 10% transportation

improvement for six unique Supply Chain Types (extraction; process manufacturing; discrete

manufacturing; design-to-order manufacturing; distribution and reselling). The above types are

differentiated by four variables: their production strategy (flow/continuous vs. batch/cellular); the

transportation mode (ship/railcar, truckload/intermodal, or LTL/small package/air); the order trigger

(make to plan, make to stock, assemble to order, make to order, or engineer to order); and the breadth

of coverage between the raw material supplier and the end consumer (US DOT, 2006).

According to current bibliography, the quantification of the values of FFTS can be approached in

two ways: by means of the factor cost approach or by modelling demand (Feo-Valero et al. 2011).

The factor cost approach estimates the value of FTTS on the basis of the decrease in cost that a

reduction in FTT entails for a transport using company. Shippers estimate all costs that can be reduced

Evangelos Sambracos and Irene Ramfou

due to a decrease in transportation time Such costs are vehicle costs dependent on time (fuel,

maintenance, tires, vehicle taxes and insurance, depreciation), drivers and maintenance workers’

wages, necessary overheads (such as training and social security payments) and in some cases the

depreciation of goods while in transit (Odgaard et al. (2005). However, costs that are not directly

related to the transport itself, such as logistics or inventory costs are not considered in this approach,

therefore ignoring cost trade-offs that will ultimately affect the magnitude of the benefit.

Behavioral models on the other hand are used mainly for modelling passenger transport demand

and consider the decision maker as a consumer of transport services. The decision maker in charge of

the shipment faces a utility maximization problem, taking into consideration parameters such as the

cost and quality of the service for each mode and the uncertainty associated to choosing that mode. In

such models, the VFTTS constitutes the marginal rate of substitution between transport time and

transport cost and is given by the estimated coefficient for time divided by the cost coefficient (Feo-

Valero et al. 2011).

Inventory models are behavioral models that incorporate variables related to production, such as

shipment size and frequency of shipment, aiming at maximizing a profit function of the transport

consumer. Baumol and Vinod (1970) were the first to introduce the inventory theoretic approach that

considers the trade-off between inventory and transportation in an effort to minimize total logistics

cost, while maintaining the necessary level of customer service and considering demand and lead time

uncertainty. In this framework the value of time for the shippers has two components: the reduction of

inventory costs occurring during transportation and the reduction of the costs of holding inventories to

respond to unexpected change in the demand.

Data for disaggregated models can be obtained by means of revealed preference or stated

preference experiments (EU, 2015). In both cases, the final objective of the researcher is to discover

how the interviewee – shipper of consignee - values transport attributes. Several authors have provided

a review of studies on the valuation of freight transport time. Feo-Valero et al. (2011) has confirmed

the dominance of SP surveys and behavioral models and have showed a remarkable variation in the

values that transport users put on FTTS. Such differences were explained partly by the different

methods adopted to collect observations and partly by the influence exerted by contextual factors such

as the trip distance, the country where the study is developed, the per-capita GDP, the category of

transported goods and the transport unit used.

Revealed Preference surveys face practical limitations basically associated with the high survey

costs, the difficulties in collecting responses for new transport services, the ambiguity of the choice set

(Ortúzar and Willumsen, 2011). Stated Preference data share the problem of “hypothetical bias” that is

the deviation from real market evidence (Hensher, 2010). This may happen due to the dependence of

the results on the capability of the researcher to conduct the survey and also the possibility that the

answer may not reflect the behavior that the respondent would adopt in a real situation. Indeed, it is

difficult to identity the decision-maker or makers in a firm. While existing approaches assume that

there is a unitary decision-making process just like in passenger surveys, when it comes to companies

there are diverse actors involved in the transport process coming from the procurement, production,

inventory, marketing or distribution department of the firm. They do not have control or knowledge of

all decisions made throughout the firm’s supply chain, especially when it comes to future decisions.

Therefore, the results of these methods are ambiguous since the same information if interpreted and

processed by a different decision rule will yield different decisions and therefore results.

3 RESEARCH METHODOLOGY

3.1 Systems Dynamics

Dynamic complexity makes it difficult to assert the effect of FTTS on transport demanding

companies. System Dynamics is a computer-aided approach for analysing and solving complex

problems with a focus on policy analysis and design. Initially introduced as Industrial Dynamics, the

field developed from the work of Jay W. Forrester at the Massachusetts Institute of Technology

(Forrester 1958, 1961). Industrial Dynamics was defined as the study of the information feedback

characteristics of industrial activity to show how organizational structure, amplification (in policies),

and time delays (in decision and actions) interact to influence the success of the enterprise.

Systems are modelled using flow rates and accumulations linked by information feedback,

forming loops and involving delays and non-linear relationships. Computer simulation is then used in

order to infer the time evolutionary dynamics endogenously created by such system structures. The

purpose is twofold: firstly to learn about their modes of behaviour and secondly to design policies that

improve performance. The essential viewpoint taken by System Dynamics is that feedback and delay

Int. Journal of Business Science and Applied Management / Business-and-Management.org

cause the behaviour of systems, i.e. that dynamic behaviour is a consequence of system structure

(Morecroft, 2015).

3.2 Developing the model

Freight transportation facilitates the processes of the procurement, the production and the delivery

of goods to their destination since it allows for the inbound transportation of production materials from

the supplier and the outbound transportation of finished goods to the customer. Freight transportation

performs an intermediary role in the internal and external supply chain providing the bridging function

between supply and demand for goods (Coyle et. al. 2010).

In this generic model a transport using company is considered that is part of a traditional supply

chain. Inventories are set according to demand information flowing upstream from the next tier of the

supply chain. For simplicity reasons we assume that the company follows a make to stock strategy and

tries to fulfill demand from current finished good inventory.

In Figure 1 the structure diagram of a typical transport using company is illustrated based on

Sterman (2000) and Morecroft (2015) depicting its internal supply chain. It consists of the stock

(represented by rectangles and act as accumulations) and flow structure of the system (represented by

arrows pointing into and out of the stock) for the ordering, receipt, storage of materials, production and

storage of finished goods and finally their delivery to customers. Also, it includes the decision structure

governing the flows that include policies for ordering production materials, scheduling production,

fulfilling orders from production and customer satisfaction.

In this generic model, decisions of all actors are considered. The company receives orders from

customers and then adjusts production in order to meet demand. Procurement managers order materials

from suppliers in order to maintain materials inventories sufficient for production to proceed at the

desired date. Apart for variations in demand they must adjust for delivery delays and possible

restrictions in order quantity. The producer maintains a stock of Ordered Materials to the supplier,

Materials Inventory, Work in Process Inventory, Finished Goods Inventory and Goods in Transit

indicating goods transported to the customer. Inflows to these stocks add to them while outflows

subtract from them, while both are subject to several decision rules. Finally, economic result of all

logistics activities is calculated as the difference between money inflows and outflows.

There are six negative feedback loops in the model forming the basis of systems perspective where

the typical thinking style is circular starting from a problem expressed as a discrepancy between a goal

and the current situation, moving to a solution and then back to the problem. Problems do not just

appear but rather spring from other decisions and actions that may have obvious or even hidden side

effects (Morecroft, 2015). The Materials Ordered Control loop adjusts Materials Order Rate in order to

move the level of the Materials Ordered to the desired level. Accordingly, the Materials Inventory

Control loop, the Production Control and Goods Inventory Control loops whose aim is to adjust

Materials Inventory Level, Production and Goods Inventory to their desired levels. The Stockout loop

of Materials and Goods regulates shipments of materials to production and of finished goods to

customers in order for the company to run production and satisfy demand respectively.

Figure 1: Analytical model structure

Materias Ordered

GAP

Desired Level of

Ordered Materials

Materials

Inventory Gap

Desired Materials

Inventory

Desired Materials Usage

Rate in Production

Desired Materials

Receipt Rate

Materials Inventory

Coverage

Actual Materials

Inventory Replenishment

Time

Finished Good

Inventory Holding Cost

Materials Cost

Revenue from

Sales

Price

Ordering Costs

Selling Price

Materials Order

Rate

Materials Order

Receipt

Total Cost -

Demand

Materials

Ordered

Materials

Inventory

Economic

Result

Materials Usage Rate

in Production

Feasible Materials Usage

Rate in Production

Feasible

Production Start

Rate

Materials Used

Per Finished

Good

Goods

Inventory

Production

Completion Rate

Shipment Rate

Production Time

Finished Good

Inventory Gap

Desired Finished

Goods Inventory

Goods in

Transit

Delivery to

Customer Rate

Delivery Tranport

Time

Desired Production

Start Rate

Expected Demand

Inventory Days of

Sale

Production

Production Gap

Desired

Production

Materials Inventory

Holding Cost

Feasible Start

Delivery Rate

Production Cost

Expected Materials

Inventory Replenishment

Time

Sourcing

Transport Time

Supplier Internal

Time

Delivery

Transportation Cost

Sourcing Tranport

Cost

Transportation

Cost

Materiasl Ordered Control

Materials Inventory Control

Production Control

Goods Inventory Control

Materials Stockout

Goods Stockout

Evangelos Sambracos and Irene Ramfou

3.3 Model assumptions and parameters setting

Several assumptions were made regarding the company’s inventory policy, production scheduling,

transportation and other operational details, as well as the finished goods’ demand. Some of them are

rather conservative but they apply in an effort to simplify the model and discuss the effects stemming

mainly from changes in freight transportation time and not in other variables. Demand for the

company’s goods is considered to be exogenous and normally distributed.

With regard to the inventory control policy, the model assumes a continuous review inventory

system where the Desired Goods Inventory and the Desired Materials Inventory depend on the

expected demand for goods from customers and materials from production and the days of coverage

the company desires to have, according to the following formulas:

Desired Goods Inventory = Expected Demand x Inventory Days of Sales (1)

Desired Materials Inventory = Desired Materials Usage Rate

+ Materials Inventory Coverage (2)

The order quantity (Materials Order Rate) placed with the upstream supplier is based on the

Materials Ordered Gap (difference between the Actual Materials Ordered and Desired Materials

Ordered), the Materials Inventory Gap (gap between Actual Materials Inventory and Desired Materials

Inventory), the Production Gap (gap between Actual and Desired Production), the Goods Inventory

Gap (gap between Goods Inventory and Desired Goods Inventory as well as on any restrictions that

exist in the materials order quantity. In the model it is assumed that there are no restrictions to the order

quantity the company can order from suppliers.

Freight transportation time affects the Materials Inventory Replenishment Time that is the total

time that elapses between placing an order to the supplier and receiving it. This time typically consists

of the time to transmit the order, the time for the supplier to process the order and have the ordered

goods ready for dispatch (considered as exogenous, since the manufacturer cannot affect it), the time to

transport the ordered goods and the time required to unload and store goods in the company’s

warehouse (considered to be minimum due to modern storage technology). For simplicity reasons it is

assumed that the Actual Materials Inventory Replenishment Time is the sum of the Supplier Time and

the Sourcing Transportation Time.

For the Business as Usual (BAU) scenario it is assumed that the Actual Materials Inventory

Replenishment Time is known to the company at all stages of simulation, and is used as an input in

order to estimate the Desired Materials Ordered to the supplier based on the thinking that the company

wants incoming orders and material inventory in order to run production during the lead time between

placing and receiving the order. Therefore:

Desired Materials Ordered = Desired Materials Usage Rate in Production

x Expected Materials Inventory Replenishment Time (4)

Desired Materials Usage Rate in production is a function of the Desired Production Start Rate and

the Materials required to produce a good. Therefore:

Desired Materials Usage Rate = Desired Production Start Rate

x Materials per Product (5)

Accordingly, transportation time affects the Delivery Time to customer along with other order

processing times that are considered to be minimum. It is assumed that goods are transported to the

customer on demand without order batching so each time the company receives an order it is

immediately served providing there is adequate inventory. Every time a shipment commences

(Shipment Rate to Customer – DRC) the stock Goods in Transit (GIT) increases until goods are

delivered to the customer (Delivery Rate to Customer - DRC). Therefore are:

∫

DRC)ds(SRC

GITGIT

 -

(6)

With regard to measuring the value of FTTS the stock Economic Result is used that is increased

by cash inflows stemming from Revenues from Sales and decrease by cash outflows stemming from

Total Cost. Total Cost is the sum of Materials Order Cost, Materials Acquisition Cost, Materials

inventory Holding Cost, Goods Inventory Holding Cost, Production Cost and Delivery Transportation

Int. Journal of Business Science and Applied Management / Business-and-Management.org

Cost. Materials Ordering Cost is the fixed cost per order irrespectively of the order quantity, Materials

Acquisition Cost is the cost of the ordered materials plus the transportation cost, Materials Inventory

Holding Cost and Finished Goods Inventory Holding Cost is the cost for holding one item in stock,

Production Cost is the cost of production and Delivery Transportation Cost is the cost for transporting

goofs to the customer.

The benefit of freight transport time savings (VFTTS) is the profit (or the loss) that the company

will enjoy after a reduction in the materials sourcing or the goods delivery transportation time.

3.4 Scenario building and simulation results

The specific parameter settings used in this model, including the initial settings for all stock are

included in Table 1. Initial values were estimated so as to ensure that the model starts with zero gaps

between the actual and the desired states of the system. Unconstrained warehouse, production and

transportation capacity is assumed in order to simplify the model, making it easier to interpret the

results that are the result of FFTS and not confounded by constrained capacity.

Table 1. Parameter settings of the model (BAU scenario)

Actual Demand (AD)

Normally distributed, Mean = 20products/day, SD = 5

products /day, maximum number of orders= 30 products

/day and minimum number of orders= 0 products /day.

Expected Demand (ED)

20 products/day

Supplier Time (ST)

2days

Sourcing Transportation Time (STT)

8days

Delivery Transportation Time (DTT)

3days

Production Time (PT)

2days

Materials Order Cost (MOC)

3€/order

Materials Purchase Price (MP) exl.

transportation cost

8€/material

Selling Price (SP) exl. transportation cost

100 €/product

Sourcing Transportation Cost (STC)

2€/material

Materials Inventory Holding Cost (MIHC)

10 €/material/year or

10/365 x MI €/day

Finished Goods Inventory Holding Cost

(FGIGCC)

20 €/product/year or

20/365 x FGI €/day

Production Cost (PC)

10 €/product

Product Delivery Transportation Cost (DCT)

5€/product

Materials Inventory Coverage (MIC)

5 days

Inventory Days of Sales (IDS)

3 days

Materials Per Product (MPP)

5 materials/product

Materials Supply Line (MSL)

Initial Value = 1000

Materials Inventory (MI)

Initial Value = 500

Work in Process (WIP)

Initial Value = 40

Finished Goods Inventory (FGI)

Initial Value = 60

Goods in Transit

Initial Value = 20

The model was simulated for 1000 days and results were produced on a daily basis (time step =

1day) using Vensim Ple software. For the BAU scenario, all parameters including transportation times

are kept constant and the Economic Result (Inflows – Outflows) is estimated. Changes in transportation

time can occur at two points affecting the Sourcing Transportation Time or/and the Delivery Transport

Time. Changes were introduced at specific time spots and several scenarios were built based on

different assumptions regarding the reorganization decisions that the company could take as a reaction

to FTTS (Table 2).

Evangelos Sambracos and Irene Ramfou

Table 2. Scenarios of FTTC and company reaction

Scenario

Sourcing

Transportation

Time - STT (days)

Delivery

Transportation

Time – DTT

(days)

Materials

Inventory

Coverage

Goods

Inventory

Coverage

Company Reaction

BAU

6 (at t=100)

EMIRT=8days at

t=110)

6 (at t=100)

EMIRT= 8days at

105)

6 (at t=100)

4 at 110

EMIRT=8days at

t=105)

6 (at t=100)

2 at 110

EMIRT=8days at

t=105)

6 (at t=100)

1 at 110

EMIRT=8days at

t=105)

6 (at t=100)

1 at 110

EMIRT=8days at

t=105)

6 (at t=100)

1 (at t=200)

6 (at t=100)

1 (at t=200)

2 at 110

EMIRT=8days at

t=105)

6 (at t=100)

1 (at t=200)

2 at 110

EMIRT=8days at

t=105)

4. ANALYSIS

Simulations of scenarios 1-10 highlight some very important conclusions regarding the economic

effect of changes in transportation time that are presented in Figures 2 and 3. It is evident that in

Scenario 6 the company has the highest economic result. In this scenario, the company reacts almost

immediately after the FTTS and adjust the expected materials inventory replenishment time according

to the saving in the sourcing transportation time and also the materials inventory coverage. The second

best solution is Scenario 10 where the company faces a reduction in inbound and outbound

transportation time, adjusts the expected materials inventory replenishment time and the materials

inventory coverage.

The worst scenarios were scenarios 1, 7 and 9 for different reasons. In Scenario 1 the company

does not change its materials ordering policy, since it does not consider the FTTS when deciding the

materials order quantity. In the other 2 scenarios, although the company adjusts its ordering policy, it

decides to reduce the finished goods inventory coverage, resulting in goods stock out and therefore

reduced sales and revenues. The effect of a reduction in Delivery Transport Time is more

straightforward to trace since it affects the Delivery to Customer Rate and consequently the Revenues

from Sales since customers pay for their ordered goods upon their receipt. A realistic extension of the

model would be to assume delivery sensitive customers and link customer delivery time to Actual

Demand. In this case the later variable will be considered to be endogenous and a function of delivery

time, assuming that customer satisfaction and ultimately demand depends of Delivery Transport Time.

However, it is difficult to fully map the link between the delivery transportation time and customer

satisfaction.

Figure 2: Economic Result for all scenarios (estimation in €)

Int. Journal of Business Science and Applied Management / Business-and-Management.org

Knowing the economic effect of every scenario it is easy to understand the value of freight

transport time savings, that is calculated as the difference between the economic result for the Business

as Usual scenario and each scenario. The VFTTS is reported in Figure 3 and shows that in Scenario 6,

the reduction in FTTS has the biggest profit for the company, while in Scenario 9 the FTTS led to a

loss.

Figure 3: The value of FTTS for all scenarios (estimation in €)

5. CONCLUSIONS

After modeling the internal supply chain of a generic company, it is evident that the answer to the

question that this paper sets in its title is negative. Savings in freight transportation time do not always

result to benefits for the company. The main reason behind that is that he behavior of a system cannot

be known just by knowing the elements of which the system is made of (Meadows, 2008).

The effect that FTTS will have on the economic result of companies depends strongly on the

decision rules they apply with regard to the ordering and the inventory policy and the time horizon of

their reorganization. Different parameters and values are expected to alter the results and lead to

different FTTS values. The above are in line with the existing theory indicating that the reactions of

companies to FTTS may include reorganization of the ordering, inventory and production policy.

A second conclusion deriving from the above is that current methods used to elicit the value of

FTTS may not safely measure this effect due to several impediments such as the existence of dynamic

complexity due to the time delays between taking a decision and its effects, the dynamicity and

nonlinearity of systems, the limited information of decision makers, the poor scientific reasoning skills,

the private agendas of decision makers leading to game playing and misperceptions of feedback

(Sterman, 2000). Al the above hinder peoples' ability to understand the structure and dynamics of

complex systems and therefore project their reaction to changes such as the ones in freight

transportation time. Simulation models provide the possibility to include estimations of difficult to

measure factors allowing the inclusion of all important parameters based on real world data or on

estimates from actors within firms.

The use of Systems Dynamics revealed several advantages compared to the traditional SP

technics. Time profiles for all variables used are returned, from the initial time until the end of the time

horizon allowing for comparisons between the BAU - and all possible scenarios. Also, the gradual

introduction of freight transport time changes is allowed along with the adaption of decision rules and

operating conditions of the firm. Moreover, simulation allows the tracing of all variables’ values and

causes behind the results on a step by step basis.

Several assumptions have been made in this article regarding the transportation, inventory and

production capacity as well as the examination of more business strategies like for example the

negotiation of minimum order quantities with the supplier, the introduction of discounts depending on

the ordering quantity, the development of a link between delivery time and customer demand. Such

inclusions in future research would make the model more complex and also more realistic.

Evangelos Sambracos and Irene Ramfou

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