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The impact of Collaborative Transportation Management on supply chain

performance: A simulation approach

Felix T.S. Chan a,⇑, T. Zhang b

a Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong b Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Pokfulam, Hong Kong

a r t i c l e i n f o

Keywords:

Collaborative Transportation Management

Performance measurement

Modeling and simulation

Supply chain management

Carriers’ flexibility

a b s t r a c t

Collaborative Transportation Management (CTM) is based on the interaction and collaboration between

trading partners and carriers participated in the supply chain, appropriate application of CTM can

improve the flexibility in the physical distribution and minimize the inefficiency of supply chain man-

agement. This paper proposes new concepts of CTM and carriers’ flexibility. A simulation approach is

used to (i) evaluate the benefits of the proposed CTM, (ii) explain the concept of carrier’s flexibility,

and (iii) optimize the delivery speed capability. Based on a simple supply chain including one retailer

and one carrier, three different simulation models have been developed with changeable delivery lead

time as follows: (1) Unconstrained delivery speed capability without CTM. (2) Constrained delivery

speed capability without CTM; and (3) Constrained delivery speed capability with CTM. Simulation

results reveal that CTM can significantly reduce the retailer’s total costs and improve the retailer’s ser-

vice level.

Ó 2010 Elsevier Ltd. All rights reserved.

1. Introduction

Global logistics in business operation has been playing a crit-

ical role in responding to the even changing market demand in

the world of globalization and mass customization. The efficiency

and flexibility of global distribution holds the key to success in

international trade. Collaborative Transportation Management

(CTM) is not only a new collaboration strategy between the ship-

per and carrier, it is also a new business model (Feng & Yuan,

2007).

In recent years, the collaboration among disparate partners

within the supply chain and e-supply chain has been widely dis-

cussed. Interestingly, the transportation and its impact on the en-

tire supply chain have seldom been explored. For instance, two

trading partners in a supply chain generally execute Collaborative

Planning, Forecasting, and Replenishment (CPFR), in order to im-

prove the inventory cost, revenue and service. However, the con-

nection with transportation and distribution management is

often neglected. Consequently, the missing link of transportation

blurs the lines between planning and execution of the supply

chain. The financial and operational performances for the sellers’

and the buyers’, therefore, would be highly affected (Bishop,

2004; Browning & White, 2000).

Can the replenishment appear at the right time and in the right

place? Often, the order is in the process, but its status is unknown

due to unavailable carrier capabilities or delayed resulted from low

carriers’ flexibility. In a high changing demand market, retailers

must suffer from high backorders of customer’s demand with high

penalty cost. Another consequence is to increase transportation

costs by using secondary carriers, whose contract rates are not as

advantageous as primary carriers. In order to minimize the ineffi-

ciency of transportation caused by insufficient interaction and col-

laboration, trading partners of the supply chain should consider

transportation management as part of the collaboration. Through

the integration and cooperation of the buyer, seller and carrier,

the flexibility and overall value of business chain would be

enhanced.

In this paper, a simple supply chain with stochastic market de-

mand will be developed including one retailer and one carrier.

Three different simulation models allowing changeable delivery

lead time will be built which present three different simulation

scenarios: (1) unconstrained delivery speed capability without

CTM; (2) constrained delivery speed capability without CTM;

and (3) constrained delivery speed capability with CTM. Different

performances of the above situations will be analysis and dis-

cussed, including retailer’s total cost and service level. The three

simulation scenarios in this paper are similar as Feng et al.

(2005), but the simulation models, software used to build the

model and indicators to measure the performance are totally dif-

ferent. The delivery capability changeable in our model is the

0957-4174/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved.doi:10.1016/j.eswa.2010.08.020

⇑ Corresponding author. Tel.: +852 2766 6605.

E-mail addresses: [email protected] (F.T.S. Chan), tingzhang930@

yahoo.com.cn (T. Zhang).

Expert Systems with Applications 38 (2011) 2319–2329

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delivery speed capability which is measured by delivery lead

time; the shorter the lead time, the higher the capability. While,

the models built by Feng et al. (2005) allowed the changeable

delivery amount, the reason of which is they focused on minimiz-

ing the inventory and the holding cost. While, one focus of this

paper is to minimize the penalty cost caused by the backorder

of customer’s demand, therefore the indicators to measure the

performance in our simulation models are the retailer’s total cost,

including penalty cost, inventory holding cost and order cost, and

the retailer’s service level.

Another main contribution of this paper is the proposal of a new

concept of carrier’s flexibility. Simulation models built in this paper

also explain the new concept of carrier’s flexibility which starts

with order/shipment forecasts including capabilities of planning

and scheduling. Briefly speaking, the main idea of carrier’s flexibil-

ity is adjustment of the planned delivery capabilities to match the

changing demand. When the demand exceeds the planned capabil-

ity of the carriers’, carriers can adjust the delivery planning strat-

egy with CTM so that the available delivery capability can meet

the demand. They can even adjust the available delivery capability

to the maximum delivery capability, in order to reduce the gap be-

tween planned delivery capability and available delivery

capability.

This paper consists of five sections. Section 1 is an introduction.

The Section 2 is the review of related literatures. Section 3 is the

development of three models of supply chain with CTM. Section

4 is the analysis of simulation results, and finally Section 5 presents

the conclusion and suggests the future research.

2. Literature review

2.1. CTM

2.1.1. Definition of CTM

According to the Collaborative Transportation Management

White Paper (2004), CTM is defined as a holistic process that bringstogether supply chain trading partners and service providers to

drive inefficiencies out of the transport planning and execution

process.

Not only is CTM a new partner strategy between the shipper

and carrier, it is also a new business model. This model includes

the carrier as a strategic partner for information sharing and col-

laboration in the supply chain. The application of CTM promises

to reduce transit times and total costs for the retailer and its sup-

pliers while increasing asset utilization for the carriers. The pro-

grams benefits all three parties involved: the retailer, the

supplier and the carrier (Tyan, Wang, & Du, 2003).

2.1.2. Objective of CTM

The objective of CTM is to improve the operating performanceof all parties involved in the relationship by eliminating inefficien-

cies in the transportation component of the supply chain through

collaboration. Transportation service represents a major compo-

nent of order lead time—the time that elapses from an order place-

ment until the goods are ultimately delivered to a customer. Much

of the variability in order lead time is attributed to variation in

transit times. With more and more companies operating on a

just-in-time basis, there is less room for error in the delivery pro-

cess (CTM White Paper, 2004).

It is important for companies to work together to eliminate

inefficiencies, reduce cost, and ensure excellence in the move-

ment of goods. In order to achieve the positive results of CTM,

the processes between participating companies should be in real

time, extendible, automated and cost-effective (Rabinovich,2005).

2.1.3. Simulation of CTM

Feng et al. (2005) developed a modified simulation model of the

‘‘beer game”, allowing the changeable delivery capacity. The

supply chain performance indicators that they measured are total

supply chain costs, including inventory costs and backlog costs,

and transportation capacity utilization. Results of the simulation

reveal that CTM can significantly reduce the total supply chain

costs and improve transportation capacity utilization.

The three simulation scenarios presented in this paper are sim-

ilar as Feng et al. (2005), but the simulation models, software used

to build the model, and indicators to measure the performance are

totally different.

(a) The inventory policy considered here is the continuous

review policy or the ‘‘s-S” policy or the fixed quantity policy.

(b) The delivery capability changeable in our proposal models is

the delivery speed capability which is measured by delivery

lead time; the shorter the lead time, the higher the capabil-

ity. While, the delivery amount is fixed.

(c) The indicators to measure the performance in simulation

models are the retailer’s total cost, including inventory hold-

ing cost, penalty cost and order cost, and retailer’s service

level.

(d) One objective in this paper is to minimize the penalty cost

caused by backorder of customer’s demand instead of the

minimizing inventory level.

2.2. Supply chain collaboration

2.2.1. Definition of supply chain collaboration

Supply chain collaboration is prevalent in today’s business

model. An organization not only optimizes itself but also collabora-

tively with other organizations to have larger optimization plan-

ning (Chan, Chung, & Wadhwa, 2004). In order to achieve an

integrative settlement, collaboration has been defined as an at-

tempt to fully satisfy the concerns of the parties involved in ex-

change (Esper & Williams, 2003). The process of collaboration,pointed by several authors, is the decision making among interde-

pendent parties ( Jiang & Jiang, 2005; Koulinitch & Sheremetov,

1998; Kwon & Lee, 2002). It involves joint ownership of decisions

and collective responsibility for outcomes (Stank, Keller, & Daugh-

erty, 2001). The key characteristics of collaboration identified are

coherence, communication, task management, resource manage-

ment, schedule management, and real-time support (Graham,

2006).

Basically, there are three types of collaborations: the horizontal,

vertical and lateral collaborations (Hsu & Hsu, 2009). The type of

collaboration is mainly decided by the collaboration scenario and

the attributes of the participants. Each type of collaborations is de-

fined below:

Horizontal collaboration: occurs when two or more unrelated or

competing organizations cooperate to share their private infor-

mation or resources, such as joint distribution centers.

Vertical collaboration: occurs when two or more organizations

such as the manufacturer, the distributor, the carrier and the

retailer share their responsibilities, resources, and performance

information to serve relatively similar end customer.

Lateral collaboration: aims to gain more flexibility by combin-

ing and sharing capabilities in both vertical and horizontal

manners.

As pointed out by Thomas and Griffin (1996), collaboration is

creating significant value in the relationships along the value

chain. Many studies have also discovered positive impact of strate-gic alliance between enterprises on their market performance

2320 F.T.S. Chan, T. Zhang/ Expert Systems with Applications 38 (2011) 2319–2329



(George, Zahra, Wheatley, & Khan, 2001; Park & Cho, 1997; Sant-

oro, Borges, & Rezende, 2006; Sarkar, Echambadi, & Harrison,

2001).

2.2.2. CTM in supply chain collaboration

A single member of the supply chain alone cannot do much to

resolve supply chain problems. This is why collaboration among

partners in a supply chain has become a topic of great interestfor many and an essential element of company strategy for others

(CTM White Paper, 2004). Previous studies on supply chain collab-

oration have focused mainly on the collaboration among supply

chain parties including the suppliers, manufacturers, wholesalers/

distributors and retailers. Such as Chan et al. (2004) and Kim,

Banerjee, and Burto (2008) studied the partnership between sup-

plier and buyer. Elofson and Robinson (2007) studied the Collective

Customer Collaboration (C3) system. As a matter of fact, supply

chain consists of not only customers in downstream flows, but also

third-party organizations, such as logistics and transportation pro-

viders (Esper & Williams, 2003; Mentzer, Foggin, & Golicic, 2000).

Researchers including Browning and White (2000), Esper and Wil-

liams (2003) and Bishop (2004) have all pointed out the need to

incorporate Collaborative Transportation Management (CTM) withCollaborative Planning, Forecasting and Replenishment (CPFR)

among trading partners in the supply chain. While CPFR is primar-

ily buyer- and seller-based, CTM involves the transportation ser-

vice providers including carriers and 3PLs to ensure efficient and

effective shipment delivery.

2.3. Lead time

2.3.1. Lead time in inventory models

The element of time was present in the earliest inventory mod-

els facing stochastic demand. For example, Karlin and Scarf (1958)

consider a multiple period model with non-zero lead-time. The

treatment of lead-time as a deterministic decision variable within

inventory models began with Liao and Shyu (1991). In their model,the order quantity is fixed so that lead-time is the only decision

variable. They introduce the concept of crashing cost to stochastic

inventory models, where crashing cost is the cost increase associ-

ated with reducing lead-time. In a series of papers beginning with

Ben-Daya and Raouf (1994), the model introduced by Liao and

Shyu (1991) is extended such that both order quantity and lead-

time are treated as decision variables. More recently, Jang and

Kleinz (2004) studied how to determine the production quantity

and the processing time as to minimize expected customer re-

sponse time and expected plant costs. Rabinovich (2005) improved

consumer direct fulfillment performance in internet retailing by

emergency transshipments and demand dispersion. Babai,

Syntetos, Dallery, and Nikolopoulos (2009) studied a single-stage

and single-item inventory system with non-stationary demandand lead-time uncertainty.

2.3.2. Time- based competition

One field of previous literature related to this paper is the time-

based competition literature, which examines delivery speed as a

source of competitive advantage. Li (1992) investigated the role

of inventory in time-based competition. Kalai, Kamien, and Rubi-

novitch (1992) studied the effect of processing speed on price

and firm’s performance, such as market share and profit. Lederer

and Li (1997) included scheduling as a strategic variable when cus-

tomers are heterogeneous. More recently, So and Song (1998)

examined competition with delivery-time guarantees. Cachon

and Harker (2002) considered competition between two firms with

price- and time-sensitive demand, and investigate the impact of outsourcing on competition. All the previous work consider the

role of delivery speed in markets where customers incur delay

costs.

The difference of this paper from previous work can be briefly

presented as below:

(a) The purpose of this research is looking for a long-term rela-

tionship between retailers and carriers to minimize the

retailers’ cost through the two parties’ collaboration, such

as mutual planning of the carrier’s capabilities and the retai-

ler’s inventory level.

(b) The objective of improvement of delivery capability is not

the competition among carriers but to minimize the ineffi-

ciency in the physical distribution process and to improve

the retailer’s performance.

(c) This research is in retailer environment, not in manufactur-

ing environment. It means that we focus on the ‘‘delivery

into the retailer”, not ‘‘delivery out of the factory”. In this

connection, the measurements are about the retailer’s

performance.

2.4. Flexibility

2.4.1. Definition of flexibility

Several attempts have been made in the literature to define,

model, and measure the flexibility with a view to understand

its true nature and its effect on the performance of the manufac-

turing system. Many definitions of flexibility can be found in the

literature. For instance, Carlsson (1989), cited flexibility as: (1)

those attributes of a production technology which accommodate

greater output variation, as the firm’s response to uncertainty,

especially in the form of fluctuations in demand, but also market

imperfections; (2) a property of initial positions as it refers to the

cost, or possibility of moving to various second period positions.

One position is more flexible than another if it makes available

a larger set of future positions at any given level of cost. Upton

(1995) defined flexibility as the ability to change with little pen-

alty in time, effort, cost or performance. More recently, Sushil(2000) defined flexibility as the exercise of free will or freedom

of choice on the continuum to synthesis the dynamic interplay

of thesis and antithesis in an interactive and innovative manner,

capturing the ambiguity in systems and expanding the continuum

with minimum time and efforts.

Flexibility becomes particularly relevant when supply chain is

considered, which consists of a network of supply, production,

and delivering firms (Christopher, 1992). In this case, many sources

of uncertainty have to be handled, such as market demand, sup-

plier lead-time, product quality, and information delay (Giannoc-

caro, Pontrantrandolfo, & Scozzi, 2003). Flexibility allows

switching of production among different plants and suppliers, so

that management can cope with internal and external variability

(Chen, Egbelu, & Wu, 1994).Based on the above definitions, it can be summarized that the

role of flexibility in a system is to enable the system to manage

the change (certain or uncertain), in an effective and efficient man-

ner. The change in the environment includes change in both the

internal environment (resource bottlenecks, etc.) and the external

environment (customer preferences, etc.). Effective manner refers

to the extent to which the effect of change has been successfully

managed, and efficiency refers to the time, cost and effort required

to do this (Wadhwa, Rao, & Chan, 2005).

2.4.2. Classification of flexibility

In spite of a large body of literature on flexibility, the major ones

are about manufacturing flexibility, for example, Browne, Dubois,

Rathmill, Sethi, and Stecke (1984) proposed eight flexibility typesto describe the nature of a manufacturing system that is still one

F.T.S. Chan, T. Zhang/ Expert Systems with Applications 38 (2011) 2319–2329 2321



of the most widely used classification of flexibility types. Benjaafar

and Ramakrishnan (1996) describe 19 types of flexibility. More re-

cently, Wadhwa et al. (2005) studied the exact mechanism that en-

ables manufacturing flexibility to reduce the lead time by

simulation experiments.

Another kind of flexibility is about supplier’s flexibility. The

flexibility of supplier for logistics can be referred to as the routing

flexibility at the shop floor level, i.e. the ability of using alternative

routes to move the work-in-process through different resources

offering the same processes (Das & Nagendra, 1997; Ho & Moodie,

1996). According to Garavelli (2003) logistics flexibility of suppli-

ers is then defined as the possibility of shifting the production of

an item (component or final product) to different sites at a given

stage, to reduce the negative impact on SCM performance. Chan,

Bhagwat, and Wadhwa (2009) focused on a similar concept of

the switching production among different flexible suppliers at dif-

ferent stages of production, with the aim of reducing the lead time

and evaluating the impact of information system and suppliers’

physical manufacturing capabilities.

However, not many literature reported on the carriers’ flexibil-

ity. One kind of related work are about flexibility shipment in the

inventory model. The earliest model with emergency shipment

may be built by Barankin (1961). In this paper, a single period

model was developed in which a shipment is received in the begin-

ning of the period and an emergency order is placed at some time

during the period. Khouja (1996) determined the profit maximiz-

ing order quantity for a single period model with an emergency

supply option and shows that this quantity is smaller than the

solution to the newsvendor model. Lau and Lau (1997a) considered

the following extension of the newsvendor model: a customer re-

ceives an order at the beginning of the season and has the oppor-

tunity to place an additional order at some point during the season.

The objective is to determine order quantities for both ordering

opportunities as to maximize expected profit, where stochastic de-

mand is represented by either normal or beta distribution. Lau and

Lau (1997b) considered the same situation with uniform demand

and compare results with and without additional replenishment.Lau and Lau (1998) extended their work involving normally dis-

tributed demand by determining the optimal reorder point, and

they include the additional dimension of set-up cost. More re-

cently, Rabinovich (2005) improved consumer direct fulfillment

performance in internet retailing by emergency transshipments

and demand dispersion.

Nevertheless, these work are from the view of flexible inventory

planning, fewwork are about the carrier’s shipment planning, even

some of them offered decision options were without actually refer-

ring to them as a form of flexibility. For example, Lau et al. (2009)

dealt with the problem of optimization of vehicle routing and pro-

posed a multi-objective evolutionary algorithm to solve the multi-

objective optimization problem. Feng et al. (2005) developed a

simulation model to meet stochastic demand of delivery amountby adjusting delivery planning but not referred the process as car-

rier’s flexibility.

This paper proposed a new concept of carrier’s flexibility and

developed three different simulation models to explain it. The

main idea of the proposed concept of carrier’s flexibility in this pa-

per is adjustment of the planned delivery capabilities to match the

changing demand.

3. The simulation model with CTM

3.1. Description of the problems

In order to reduce the high penalty cost caused by the de-mand backorders, the retailer has to re-engineer the process of

an enterprise. The focus should be on the collaborative of logis-

tics strategies, sharing information in the supply chain and

improvement of delivery flexibility. When the retailer’s planned

delivery lead time shorter than the planned capability of the car-

riers, carriers can adjust the transportation planning strategy

through CTM so that the available delivery capability can match

the retailer’s demand. They can even adjust the available deliv-

ery capability to the maximum available delivery capability, in

order to reduce the gap between planned delivery lead time

and available delivery lead time. This paper skips the description

of various negotiation details of the partners of CTM and directly

refers to the result of transportation collaboration, because this

paper focuses more on constructing the simulation models with

CTM.

3.2. Simulation logic

Three models will be built to simulate three different situations

as follows:

Model one: unconstrained capability without CTM.

Model two: constrained capability without CTM.Model three: constrained capability with CTM.

The simulation logic is divided into two sessions: one is demand

generation as show in Fig. 1; the other is order processing and

shipping as presented in Figs. 2–4. The demand generation sessions

are the same in three models, while the differences are presented

in the order processing and shipping.

Fig. 1 shows the demand generation session. The process begins

at each simulation day with the generation of an entity that repre-

sents a customer. Then that customer’s demand is generated. The

model then determines whether the demand can actually be filled.

If there is enough inventory, the demand can be filled and be taken

away from inventory; if not, the demand backorder along with its

penalty cost are recorded. The cost and service level statistics will

be updated accordingly.

Fig. 2 presents the order processing and shipping in model one.

Since continuous review inventory policy is considered here, the

model checks inventory level at each simulation day. Then it com-

pares the inventory level with the reorder point. If the inventory le-

vel is lower than the reorder point, the order processing begins

with a required delivery lead time. There is unconstrained delivery

capability in model one, so the actual delivery lead time (Ti) is the

required delivery lead time (Di). After time Ti shipment, the inven-

tory level is updated.

Fig. 1. Demand logic.




Fig. 3 presents the order processing and shipping in modeltwo. The part before the order processing begins is similar as

model one. Since, there is constrained delivery capability in mod-

el two which is presented by constrained delivery lead time avail-

able (Vi), the model has to determine whether the required

delivery lead time can be met. If Di is not shorter than Vi, the ac-

tual delivery lead time Ti will be equal to the requirement Di. On

the other hand, if Di is shorter than Vi, then Ti will be equal to Vi

which is longer than Di.

Fig. 4 shows the order processing and shipping in model three.

The part before the order processing begins is still similar as mod-

els one and two. In model three, there is constrained delivery capa-

bility with CTM, so the required delivery lead time Di can bemet as

long as it is not shorter than the shortest delivery lead time u

which presents the maximum delivery capability.

3.3. Variables

3.3.1. Notation

Di required delivery lead time at cycle i

Ti actual delivery lead time at cycle i

Vi available delivery lead time at cycle I ; Vi $ unif(u, U )

u shortest delivery lead time, also the lower limit of

available delivery lead time ay cycle iU the upper limit of available delivery lead time at cycle i

TD(t ) total customer demand

FD total immediately filled customer demand

f (t ) immediately filled customer demand everyday

I (t ) total on-hand inventory

inv(t ) on-hand inventory everyday

B(t ) total backorder

b(t ) backorder everyday

Q replenishment quantity

Q 1 order-up-to-level, which refers to ‘‘S” in the ‘‘S-s

policy”, also the initial value of on-hand inventory

Q 2 reorder point

S.s safety stock

H (t ) total holding cost

h holding cost per unit per day

P (t ) total penalty cost

p penalty cost per unit per day

O(t ) total order cost

fc fixed order set-up cost

o(t ) delivery cost per order

C (t ) total cost

ô service level

li mean demand in one cycle; li $ norm(m, ð12)

m mean value of mean demand per cycle

ð1 standard deviation of mean demand per cycle

k(t ) customer demand everyday; kt $ norm(li, ð22)

ð2 standard deviation of everyday’s customer demand

3.3.2. Initial values

The initial values of above variables are as follows:

m 10 units

ð1 3 units

ð2 1 unit

Q 1: 310 units

Q 2: 80 units

S.s 10 units

h $0.025

p $2.5

fc $10

3.4. Model formulation

3.4.1. CTM

Di ¼ I ðt Þ=li: ð1Þ

Refer to Eq. (1), the required delivery lead time (Di) is the on-hand

inventory divides by the mean demand in that cycle (li).

Vi $ unif ðu; U Þ: ð2Þ

Refer to Eq. (2), the available delivery lead time (Vi) is random gen-

erated between shortest delivery lead time (u) and longest delivery

lead time (U ) according to the uniform distribution.,

Ti ¼ Di; if Di=Vi: ð3Þ

Ti ¼ Di; if u5Di5Vi: ð4Þ

Ti ¼ u; if Di < u: ð5Þ

Fig. 2. Model one.

Fig. 3. Model two.




Refer to Eq. (3), the actual delivery lead time (Ti) is equal to the re-

quired delivery lead time (Di) when Di is longer than the available

delivery lead time (Vi). The carrier need not adjust the delivery

planning.

Refer to Eq. (4), the Di can still be met even when it is shorter

than Vi as long as it is longer than the shortest delivery lead time

(u). The carrier can shorten the Vi to Di through the CTM.

Refer to Eq. (5), the Ti equals to u when Di is too short. The car-

rier can only adjust the Vi to u.

The limit condition in Eqs. (1)–(5) is as below:

Di; Ti; Vi; U ; u = 0:

Ti; Vi = u:

The actual delivery lead time (Ti) and the available delivery lead

time (Vi) the carrier can supply cannot be shorter than the shortest

delivery lead time (u).

u 5 Vi 5 U :

The available delivery lead time in cycle i (Vi) is between the lower

limit (u) and the upper limit (U ).

3.4.2. Demand and inventory

li $ norm m;ð12

: ð6Þ

Refer to Eq. (6), the mean demand in one cycle (li) is a stochastic

variable and follows the normal distribution with the mean of m

and the standard deviation of ð1.

kt $ norm li; ð22

: ð7Þ

Refer to Eq. (7), the customer demand everyday in one cycle (kt ) fol-

lows the normal distribution with the mean of li and the standard

deviation of ð2.

TDðt Þ ¼

Z t

0

kðt Þ Â dt : ð8Þ

FDðt Þ ¼

Z t

0

f ðt Þ Â dt : ð9Þ

Refer to Eq. (8), total customer demand (TD(t )) equals to the cumu-lation of customer demand everyday (k(t )).

Refer to Eq. (9), total immediately filled customer demand

(FD(t )) equals to the cumulation of the part of everyday’s customer

demand which can be met immediately( f (t )).

I ðt Þ ¼

Z t

0

inv ðt Þ Â dt ;

inv ðt Þ ¼ iðt Þ À kðt Þ: ð10Þ

Refer to Eq. (10), total on-hand inventory (I (t )) equals to the cumu-

lation of inventory everyday(int(t )).

In which, inventory everyday(inv(t )) = inventoryÀ demand

Bðt Þ ¼

Z t

0

bðt Þ Â dt ;

bðt Þ ¼ kðt Þ À iðt Þ: ð11Þ

Refer to Eq. (11), total backorder(B(t )) equals to the cumulation of

backorder everyday(b(t )).

In which, backorder everyday(b(t )) = demand-inventory

Q ¼ Q 1À Q 2: ð12Þ

Refer to Eq. (12), replenishment quantity (Q ) is the difference be-

tween the order-up-to-level of inventory (Q 1)and the reorder point

(Q 2).

3.4.3. Cost structure

H ðt Þ ¼ hÃI ðt Þ: ð13Þ

P ðt Þ ¼ pÃBðt Þ: ð14Þ

Oðt Þ ¼ fc þ oðt Þ: ð15Þ

In which o(t ) = k/Ti.

Refer to Eq. (13), Holding cost(H (t )) equals to holding cost per

unit per day(h)*accumulative on-hand inventory(I (t )).

Refer to Eq. (14), Penalty cost(P (t )) equals to penalty cost per

unit per day( p)*accumulative demand backorder(B(t )).

Refer to Eq. (15), Order cost(O(t )) equals to fixed order set-up

cost( fc ) plus delivery cost(o(t )).

In which, delivery cost = k/actual delivery lead time, and k is aconstant parameter.

Fig. 4. Model three.


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3.5. Supply chain performance measurements

Two performance indicators as below:

C ðt Þ ¼ H ðt Þ þ P ðt Þ þ Oðt Þ; ð16Þ

o ¼ FDðt Þ=TDðt Þ: ð17Þ

Refer to Eq. (16), Total cost(C (t )) equals to the sum of holding cost,

penalty cost and order cost.Refer to Eq. (17), Service level(ô) equals to filled customer de-

mand/ total customer demand.

3.6. Assumptions

(a) To facilitate the control of simulation model, the length of

one simulation cycle (i) is one month which equals to 30

simulation days.

(b) From test the simulation steady state by changing the simu-

lation length in certain initial conditions and random num-

bers, we observe the simulation results and found that the

model reaches steady-state after three simulation years,

therefore, we set the total simulation length ten years, whichequals to 3600 simulation days, to guarantee the model run

for a enough long time.

(c) There is only one delivery demand in one simulation cycle

and there is no delivery backorder. This assumption is simi-

lar to one made by Hadley and Whitin (1963, p. 162), in their

analysis of the single lead time case.

(d) Lead time has two parts—order lead time and delivery lead

time. To simplify the model, we assume order lead time is

zero, so the lead time in our paper refers to the delivery lead

time. The main factor makes impact on the delivery lead

time is the delivery speed capability of the carrier.

(e) Order cost is the sum of fixed set-up order cost which is con-

stant and delivery cost which is the expression of actual

delivery lead time. Therefore, the order cost is only affectedby one decision variable, that is the delivery lead time. In

fact, the shorter the delivery lead time, the higher the order

cost.

4. Results analysis

4.1. Comparison of the three models

The results of simulation are shown in Table 1.

The service level in model one (0.98393) is higher than both

model two (0.87984) and model three (0.94393). The reason is in

model one, there is unconstrained capability, i.e. the required

delivery lead time is the actual delivery lead time, the backorder

level is low. But the service level isn’t one hundred percents inmodel one because of the deviation of customer demand.

The total cost in model one (26,735) is lower than model two

(32,890), because of the low penalty cost, but higher than model

three (26,364), because of the high order cost. It indicates that

it’s possible to trade off among the major cost components and find

the best solution of total cost.

There is constrained capability in both the models two and

three, but model three is with CTM. The delivery forecasting infor-

mation is shared so that the available delivery capability can be ad-

justed to the maximum delivery capability, which will reduce the

demand backorders, resulting in a low penalty cost. Hence, the to-

tal cost in model three is lower than that in model two.

4.2. Improvements of CTM

In the following section, constrained capability scenario will be

discussed to study how CTM improves the total cost and service le-

vel along with the time.

Fig. 5 indicates the total cost with CTM is lower than that with-

out CTM. Moreover, the gap is wider along with the time.

Fig. 6 presents the service level with CTM is obviously higher

than that without CTM. Furthermore, both of the two service levels

converge finally (0.94393 with CTM and 0.87984 without CTM).

One point that should be noticed here is the two curves reach

the steady-state around 1000 simulation days, however, the gap

between the two situations is still wide even before the curves

reach the steady-state.

4.3. Sensitivity analysis

The slope of a curve refers the dependent variable’s sensitivity

to independent variable. For example, when we set the dependent

variable is total cost and the independent variable is delivery capa-

bility, the steep curve indicates the change of delivery capability

affects heavily on the total cost, i.e. the total cost is very sensitive

to delivery speed capability.

In the following section, we study the sensitivity of total cost

and service level to three parameters—the maximum level of deliv-

ery speed capability, the penalty cost per unit per day and the stan-

dard deviation of customer demand per cycle, in order to study

how the CTM improve the supply chain performance when theseimportant parameters change.

4.3.1. Change the maximum level of delivery speed capability

We will measure delivery speed capability by delivery lead

time, i.e. shorter lead time means higher capability. We change

the shortest delivery lead time but the longest one is infinite. In

the situation with CTM, it means that the carrier can meet the re-

quired delivery lead time when it is not shorter than the shortest

delivery lead time. While in the situation without CTM, it means

that the carrier’s available delivery lead time changes among the

shortest delivery lead time and a fixed delivery lead time, 10 days

in this case, according to the uniform distribution. When we adjust

the penalty cost per unit per day, we fix the holding cost per unitper day.

Fig. 7 indicates the total cost increases regardless of application

of CTM or not. However, it is obviously improved when CTM is

adopted, especially in the middle stage. But in the last stage which

means the delivery capability is too low, the total cost of two

curves converge. Another point we should notice is the total cost

increases with the delivery lead time all the time if there is no

CTM. While, if there is CTM, the total cost reaches a bottom around

six days, which means we can get a ‘‘best solution” in some deliv-

ery lead time. In this case, the best solution is the delivery lead

time of six days in which the total cost is 25,642, saving 21% than

31,052 in the situation without CTM.

Fig. 8 indicates the service level is obviously improved when

CTM is adopted, especially in the middle stage. Similar to Fig. 7,in the last stage, the curves converge.

Table 1

Comparison of the three models.

Model One Two Three

Capability limit No Yes Yes

CTM No No Yes

Total cost 26,735 32,890 26,364

Service level 0.98393 0.87984 0.94393


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4.3.2. Change penalty cost per unit per day

Fig. 9 indicates the total cost in both situations increase whenthe penalty cost increase. While, in the situation without CTM,

the increase is much higher than the situation with CTM which

indicates the total cost without CTM is more sensitive to penaltycost. It suggested that the CTM will improve the total cost

Fig. 5. Change of the total cost with.

Fig. 6. Change of the service level with time.




obviously when the penalty cost is high, for example, the penalty

cost can be very high in a high competition environment; if we

cannot meet the customer’s need once, we will lose the customer

for ever.

4.3.3. Change standard deviation of customer demand per cycle

Fig. 10 indicates the total cost increases with the deviation of

customer demand per cycle. While, similar to Fig. 9, the increase

is quicker in the situation without CTM. It suggests that CTM will

obviously improve the total cost when there is a high unstable de-

mand, for example, the demand fluctuates heavily due to the sea-sonal effect in the market.

4.4. Optimum delivery speed capability in CTM model

High delivery capabilities cost a lot for carriers, and it is a huge

waste if they are not necessary, while, the shipment planning and

demand will be hardly met if the delivery capabilities are too low,and the delivery backorders will also result in huge cost for carri-

ers. In the past, the planning of proper delivery capabilities is very

difficult resulted from lack of communication and information

sharing between carriers and retailers, while, the collaboration of

the two parties facilitates the planning work, and the seeking of

optimum delivery capability becomes possible.

Now, let us examine the question what’s the optimum delivery

speed capability the carriers should supply in CTM scenarios. This

question can be addressed under two fields, one is to analyze how

the major cost components change with delivery speed capability.

Those cost components changing significantly are the ones sensi-

tive to delivery speed capability, hence, it is possible to get the

optimum delivery speed capability when these cost components

are tightly controlled. The other field in this section is to observe

the total cost’s sensitivity to delivery speed capability in different

situations with various penalty cost per unit per day and various

standard deviation of customer demand per cycle. The optimum

delivery speed capability is different in different cost structure

and demand environments, hence, the carriers should make the

optimum planning according to the real world.

4.4.1. Sensitivity of major cost components

Two important points will be considered—one is how the

amount of the major cost components change with delivery speed

capability, the other is how their sensitivity change. It should be

noted that we measure the delivery capability with delivery lead

time.

Fig. 11 indicates the holding cost decreases with the deliverylead time. The reason is the replenishment amount is fixed, so

the inventory level decreases when the order cycle is long.

The order cost also decreases. The expression of order cost tells

us the same information.

The penalty cost increases sharply which indicates it is very

sensitive to the delivery speed capability.

4.4.2. Sensitivity of total cost in various penalty cost

In the following part, how the total cost’s amount and sensitiv-

ity to delivery lead time change in different situations with various

penalty cost, will be discussed.

Fig. 12 presents how the total cost changes with the delivery

lead time in three situations—the penalty cost per unit per day in-

creases to 100%, 200% and 300% of the initial value. It can be ob-served the total cost in each curve almost keeps the same when

Fig. 7. Sensitivity of total cost to delivery lead time.

Fig. 8. Sensitivity of service level to delivery lead time.

Fig. 9. Sensitivity of total cost to penalty cost.

Fig. 10. Sensitivity of total cost to deviation.




the delivery lead time is before six days, which means the total cost

is not so sensitive to delivery lead time. It suggests that merely

fromthe view of penalty cost, to decrease delivery lead time to less

than six days does not help a lot to save total cost, moreover, it will

be a waste. While, it can also be observed that the total cost in-

creases sharply after seven days in all the three curves, further-

more, the gaps among the three curves become wider along with

delivery lead time, which means the total cost is more sensitive

to delivery lead time when the penalty cost increases. Hence, it

suggests that to improve the delivery capability is very important

when the available delivery lead time is longer than seven days

and the improvement is more pressing when the penalty cost is

high.

4.4.3. Sensitivity of total cost in various customer demand deviation

In the following part, the impact of the standard deviation of

customer demand per cycle will be discussed.

Fig. 13 presents how the total cost changes with the delivery

lead time in three situations – the standard deviation of cus-

tomer demand per cycle is one, two and three, respectively. It

can be observed that the total cost increases with the deviation

and the lowest total cost reaches at an earlier point (day eight

when deviation is one; day seven when deviation is two; day

six when deviation is three) which means the optimum delivery

lead time should be less when the deviation increases. It sug-

gests that to improve the delivery capability is very important

when the demand is unstable.

5. Conclusions and suggestion of future research

CTM is based on the collaboration between trading partners

and carriers of a supply chain in order to minimize the ineffi-

ciency of physical distribution and improve the flexibility of the

supply chain. Currently, more and more partners in the supply

chain operate on a just-in-time basis. With the expectation of

shortening planning cycles and minimizing demand backorders,

transportation efficiency becomes one of the crucial factors for

efficient supply chain management. It is important that business

partners cooperate to strengthen communication, share informa-

tion, and ensure the efficiency of physical delivery. That is why

collaboration among partners in a supply chain has become a to-

pic of great interest for many and an essential element of com-

pany strategy.

The purpose of this research is looking for a long-term relation-

ship between retailer and carrier to minimize the retailer’s total

cost through the two parties’ collaboration. The objective of opti-

mization of the delivery capabilities planning is to improve the re-

tailer’s performance.

This paper proposes new concepts of CTM and carriers’ flexibil-

ity. A simulation approach is used to evaluate the benefits of theproposed CTM, explain the concept of carrier’s flexibility and opti-

mize the delivery speed capability. Results of the simulation reveal

that CTM can significantly reduce the retailer’s total costs and im-

prove its service level. Not as many previous papers in which the

holding cost made a heavy impact on the total cost, the perfor-

mance indicators in our models are sensitivity to the penalty cost,

the reason of which is our models are based on a high changing de-

mand market.

Simulation models built in this paper also explain the new con-

cept of carrier’s flexibility which starts with order/shipment fore-

casts including capabilities planning and scheduling. Briefly

speaking, the main idea of carrier’s flexibility is adjustment of

the planned delivery capabilities to match the changing retailer’s

shipment demand. The simulation results demonstrated that themutual planning of the carrier’s capabilities with CTM can improve

carrier’s flexibility.

The future research is suggested in the following aspects:

(a) Development of a multi-echelon supply chain with the man-

ufactures, distribution centers, carriers, and retailers verti-

cally and more than two competitors horizontally.

(b) The performance indicators are related to each partner in the

supply chain, respectively, and the whole supply chain as

well.

(c) In the horizontal supply chain, some competition issues

should be researched, such as whether the retailer or the

carrier has the right to decide the order cost, application

of CTM as the completion advantage between two carriers,etc.

Fig. 11. Sensitivity of major cost components to delivery lead time.

Fig. 12. Change of total cost with delivery lead time when penalty cost differs.

Fig. 13. Change of total cost with delivery lead time when deviation differs.




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