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Assessing performance factors for a 3PL in a value chain

Gulgun Kayakutlu, Gulcin Buyukozkan n

Galatasaray University, Istanbul 34357, Turkey

a r t i c l e i n f o

Article history:

Received 27 February 2009

Accepted 12 October 2010Available online 31 December 2010

Keywords:

Third party logistics (3PL) companies

Logistics performance factors

Analytic network process

Value chain performance

a b s t r a c t

Business continuity of the logistics companies in the twenty first century highly depends on the value

chain performance. As the variety of services outsourced to thirdparty logistics(3PL) companies increase,

success strategies for these companies are to be revised. This study explores and illustrates an analytical

framework to assess the performance factors for 3PL companies through a managerial view. The factors

integrating the strategical and operational targets are evaluatedwithin a framework based on four levels;

performance targets, planning activities, logistics operations, and performance attributes of logistics

operations. The analytic network process is used to determine the most effective performance attributes.

The framework is applied and studied in two major logistics companies active in the South East Europe.

The proposed framework will contribute to the logistics sector by demonstrating the paradigm shift in

performance measurement.

& 2011 Published by Elsevier B.V.

1. Introduction

Increasing requests for logistics services imposed a strategic role

for the third party logistics (3PL) companies. It has been emphasised

by many researchers that supply chain will not be effective unlesslogistics firms do notmeasureand monitorthe companyperformance

in a flow offunctions rather than individual activities (Robertson et al.,

2002). The biggest pace is taken by integrated evaluation of informa-

tion andmaterial flow(Gunasekaran and Ngai, 2003).It isshownby a

recent literature survey on logistics and supply chains that there is

still a big gap on reconsidering inter-functional and inter-company

measures (Sachan and Datta, 2005). These gaps are advocated by

concentration of 3PL companies on outsource requests; which are

focused on evaluation of service provider on a single function such as

transportation and warehousing ( Jharkharia and Shankar, 2007). The

need for differentiating proposed services caused managers to ask for

quantitative performance scores (Cook and Bala, 2007). Hence, there

is a necessity of considering variations arising across the domain of

‘‘effective factors’’ (Parhizgaria and Gilbert, 2004); as well as integrat-ing supply chainmanagement and logistics management (Kim,2009).

In the production economy and business strategy literature,

considerable interest has been centred on identifying the domain of

effective factors. Approaches show a variety of dimensions in defining

the success, such as quality and organisational interactions (Cheng

et al., 2005), integrating network and operational strategies (Rudberg

and Olhaberg, 2003), relating marketing performance and human

capital (Knemeyer and Murphy, 2004), supply chain strategies and

logistics operations (Lai et al., 2004; Liu and Ma, 2005; Jayaram and

Tan, 2010). First generic covering was realised by Yamin et al. (1999).

Since then, there hasbeen some industry specificanalysis as theone on

automotive logistics by Schmitz and Platts (2004), Krakovics et al.

(2008) and in food processing as in Hsiao et al. (2010).Today the altitude of performance is defined by core compe-

tencein networks,process orientation, freemargins,organisational

learning and technology utilisation as Gunasekaran and Ngai

(2007) specifies. To create competitive advantages based on these

new fields of focus, detailed factors vary by industry. De Sensi et al.

(2007) makes an introduction to the industry specified issues in

beverage supply chains. Singh et al. (2005) make the analysis in

automotive industry of Australia. The article of Laiet al. (2007) take

the issues in 3PL companies considering the clusters in China.

South East Europe has become an important hub for logistic

services between Asia and Europe. Hence, 3PL logistics companies

giving services through Europe are in the process of changing the

businessparadigm. This is thefirst study that will discoverthe factors

that need to be considered in competitive strategy reengineering byTurkish partnered companies that take role in this important route.

This study has two main objectives: (1) define a model to analyse

the effectiveness of a variety of factors that will link strategical and

operational targets using Analytic Network Process (ANP); (2) apply

the framework to compare effectiveness of the factors in two major

3PL companies of South East Europe with different strategies.

Managers of the companies surveyed are in the process of changing

the strategies and have not yet determined exact responses for the

business questions. It is an obligation to prepare and present a pool

of factors affecting the competitive strategies and then study the

interdependencies and effectiveness of those factorsfor thecompanies

studied.

Contents lists available at ScienceDirect

journal homepage: www.elsevier.com/locate/ijpe

Int. J. Production Economics

0925-5273/$ - see front matter & 2011 Published by Elsevier B.V.

doi:10.1016/j.ijpe.2010.12.019

n Corresponding author. Fax: +90 212 259 5557.

E-mail address: [email protected] (G. Buyukozkan).

Int. J. Production Economics 131 (2011) 441–452

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http://dx.doi.org/10.1016/j.ijpe.2010.12.019

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There is an unavoidable need to apply a multi criteria methodto

convert the managerial opinions into figures and analyse the

dependency among the factors. Though there are several methods

that canrepresent theimportance andrank of thefactors in figures,

ANP is chosen for its unique features in analysing the interdepen-

dent criteria.

Thepaper is organised as follows. Thesecond section is reserved

for preparing the pool of factors through literature survey. ANP

methodwill be explained in the third section. In the fourth section,the application of the proposed framework will be presented. The

conclusion and further recommendations will be given in the fifth

and last section. The proposed framework will lead the managers of

3PL logistics sector by demonstrating the paradigm shift in perfor-

mance measurement.

2. Proposition of a conceptual framework

2.1. Background for the conceptual framework

Literature survey is run to depict the performance factors con-

sidered in logistics industry with the goal of global competition.

Almost a hundred articles printed in highly graded academic period-

icals are reviewed, which try to solve different issues in developing

competitive uniqueness, recommending analytical and/or heuristic

models for solutions. It is observed that strategic goals embracing

competitive advantage in a supply chain can be classified in three

groups: networking, capital balancing and customer focus. Effective

planning enables linking these strategies with different logistics

operations. There is a big variety of measure for operational perfor-

mance. Besides, control and coordination of planning will feed in the

right information for the related strategies (Liu and Ma, 2005). This

section is organised to handle performance factors at strategic,

planning and operationallevel and the summary of literature analysis

is given in Table 1a.

2.1.1. Performance targets

The evolution in information sciences lead majority of academic

studiesto focus on business networkissues. Boyson et al. (1999) is one

of the initiators of discussion on alliance relationship in distribution

networks being as influential as cost management. Business alliancesconsider distribution channels as well as supplier and partner

relations. Stock et al. (2000) detail the issues of distributors in a

supply chain, emphasising that performance influencers are not only

configuration and organisation of the chain, as mentioned by

Jayaraman and Ross (2003), but real partnering in planning, pricing

and services. Dubois and Gadde (2000) reveal the innovation and

efficiency advances by partnering with suppliers. Ross and Droge

(2004) worked on strategies and operations effected by supply chain

efficiencies. It is analytically observed that effectivebusiness network

would grant strategic enhancements as well as operational efficiency

improvements. Most recently each operation is tested in detail to

realise the integration (Dong and Chen, 2005; Biehl et al., 2007). It is

observed that the supply chain is to be designed based on structural

and relational coordination criteria as stated by Truong and Azadivar

(2005). Kim (2009) gets into the details of supply chain management

influences on structured and unstructured integration of strategies

and logistics operations.

Since logistics is a value adding industry, capital balancing is an

important target specifically in developing countries (Clark et al.,

1993). One of the major operations of logistics, inventory manage-

ment needs special interest in strategic and operation planning

since it is a highly capital binding operation. Bonney (1994)

analysed how capital performance is interacted with the balance

of pull and push strategies of inventorymanagement. DeSensi etal.

Table 1a

Summary of literature review.

Performance

factors

Relevant focus Main references

Strategic targets

Networking Distribution network Stock et al. (2000), Jayaraman and Ross (2003), Liu and Ma (2005), Sachan and Datta (2005)

Logistics chain (network) Boyson et al. (1999), Dong and Chen (2005)

Supply chain (network) Dubois and Gadde (2000), Ross and Droge (2004), Truong and Azadivar (2005), Biehl et al. (2007), Kim (2009),

Jayaram and Tan (2010).

Capital balance Structural capital Gunasekaran et al. (2005), De Sensi et al. (2007)

Human capital Clark et al. (1993), Rudberg and Olhaberg (2003), Knemeyer and Murphy (2004), Gunasekaran et al. (2005)

Financial capital Clark et al. (1993), Bonney (1994), Ross (2000), Gunasekaran et al. (2005), Krakovics et al. (2008)

Relational capital Zhao and Stank (2003), Gunasekaran et al. (2005)

Customer focus Quality-customer satisfaction Andersson et al. (1989), Fawcett and Cooper (1998), Korpela and Lehmusvaara (1999), Ross (2000),

Goetschalckx et al. (2002), Rudberg and Olhaberg (2003)

Mass customisation Rabinovich et al. (2003)

Customer capital Barad and Sapir (2003), Lai and Lee (2003), Zhao and Stank (2003), Parhizgaria and Gilbert (2004), Kuˇ sar et al.(2005), Krakovics et al. (2008)

Customer segmentation Mentzer et al. (2004), Kuˇ sar et al. (2005)

Demand chain Landeghem and Vanmaele (2002), Treville et al. (2004), Cheng et al.(2005), Hsiao et al., (2010).

Accredited customers Bottani and Rizzi (2006)

Planning activities

Strategies Technology/organisation Andersson et al. (1989), Hameri and Paatela (1995), Schmitz and Platts (2004), Kim (2009).

Alliances/customers Robertson et al. (2002), Hertz and Alfredsson (2003), Georgiadis et al. (2005), Sachan and Datta (2005)

New product/services Hertz and Alfredsson (2003), Rudberg and Olhaberg (2003)

Resources Distribution centres Toppen and Smits (1998), Gunasekaran and Ngai (2003), Zhao and Stank (2003), Bogataj and Bogataj (2004),

Ross and Droge (2004), Georgiadis et al. (2005), Krakovics et al. (2008), Hsiao et al., (2010)

Delivery vehicles Toppen and Smits (1998), Ross and Droge (2004), Georgiadis et al. (2005), Ioannou (2005)

Employees Gunasekaran and Ngai (2003), Cook and Bala (2007), Ioannou (2005)

Information Strategic planning Yamin etal. (1999), Gunasekaranand Ngai(2003, 2004), Yusufet al.(2004), Bayraktar et al. (2009), Kim (2009).

Operational planning Toppen and Smits (1998), Au et al. (2002), Kim and Narasimhan (2002), Landeghem and Vanmaele (2002),

Rabinovich et al. (2003), Kim (2009).

Measurement Irani et al. (2006), Hamdan and Rogers (2008)

G. Kayakutlu, G. Buyukozkan / Int. J. Production Economics 131 (2011) 441–452442



(2007) worked on the importance of inventory issues in the supply

chain. Another capital balancing issue is the resource allocation

influencing the distribution centres and transportation facilities as

Ross (2000) has discussed. There are not many researches that

combine financial or even cost issues with the structural and the

relational capital. Gunasekaran et al. (2005) is a pioneer insearch of

performance factors of the twenty first century, where new factors

are defined considering the integrated analysis of all capitals.

Customer focus is found to be the third major goal. Customersatisfaction has been considered as a performance factor in all

industries in the last ten years of twentieth century (Anderson

et al., 1998). The need to redesign internal and external customer

processes have been a majorconcern in logistics(Fawcett and Cooper,

1998). Customer satisfaction concept was analytically expressed

only after the development of qualitative methods (Korpela and

Lehmusvaara, 1999). Measures used in relations enabled the asso-

ciating customer capital with cost and profit performances(Anderson

et al., 1998; Fawcett and Cooper, 1998; Korpela and Lehmusvaara,

1999; Goetschalckx et al., 2002). Development of supply chains led

the target of mass customisation which needs detailed demand

analysis to include both enterprise and relational data (Rabinovich

et al., 2003). Customer relations became an important asset only after

definition of customerrelatedprocessesin operation (Barad andSapir,

2003; Lai and Lee, 2003; Zhao and Stank, 2003). Customer segmenta-

tion helped in strengthening the focus (Mentzer et al., 2004). Hence,

contribution of this asset in cost reduction, lead time minimisation

and capacity optimisation are also measured (Kuˇ sar et al., 2005). It is

also emphasised that linking customers in a demand chain will con-

tribute more in bothstrategic and operationalmodelling (Landeghem

and Vanmaele, 2002; Treville et al., 2004). Positive interactions with

loyal customers are expected to influence total quality of the

organisation (Cheng et al., 2005). As outsourcing increased customer

orientedservice performancehas become the challenge. 3PL firms are

imposed to focus on accredited customers for sustainability (Bottani

and Rizzi, 2006). Even cost optimisation has to be revised based on

services (Ross et al., 2007).

2.1.2. Planning activities

The main objective of logistics is overall coordination of strategies

andoperations. Thecoordinationcanbe realised by theright planning;

there is a need of effective plans for resources and information in

parallel with the altering strategies (Andersson et al., 1989).

The increasing interest in 3PL operations by companies from

different industries changed the traditional way of planning from

operation integration to integrating multiple global strategies. With

the aid of technology it has been easier to simulate the different

strategic models for integration (Hameri and Paatela, 1995). These

models are expected to include managerial performances to incorpo-

rate the link of customers and alliances to provide more complex

services (Hertzand Alfredsson,2003). Briefly, plans areexpectedto find

solutions for multi-level supply-chain issues (Georgiadis et al., 2005).Resource based performance theories are focused on limits and

optimisation of capacities (Zhao and Stank, 2003). Resource plan-

ning is interrelated with operational strategies and gets into more

detail with improvements in enterpriseplanning software (Bogataj

and Bogataj, 2004). It is observed that distribution centres, distri-

bution vehicles and employees are the main issues in resource

planning. Optimisation of resources is effective in operational cost

performance (Ioannou, 2005) in terms of effective structure of

distribution network and efficient utilisation of information about

the resources (Toppen et al., 1998).

Yamin et al. (1999) have stressed the effect of information

planning on logistics performance that helps analysing organisa-

tional success. Studies on functional improvements by information

planning, such as cost reduction (Toppen et al., 1998), lead time

reduction (Rabinovich et al., 2003) and customer satisfaction

(Au et al., 2002; Kim and Narasimhan, 2002; Treville et al., 2004)

enlightened the paradigm change in logistics operations. Devel-

opment of supply chains and the need foragility are emphasised in

studies of Gunasekaran and Ngai (2003, 2004), who succeeded the

promotion of information and material flows in parallel. These two

researches have contributed to remove the restricted vision of

operational data. It can be extended to benefit information and

knowledge by effective planning of infrastructure, inter-operationand relational information (Yusuf et al., 2004). Consequences of

information plans are to be measured with care since they are in

interaction with strategic and operational plansin both dimensions

(Irani et al., 2006). Recently the impact of information systems on

the supply chain of manufacturing companies is emphasised by

Bayraktar et al. (2009).

It is observed in the literature of planning activities, that, it is

indispensable to run and implement strategic, resource and

information plans together as a bond between the strategies and

the operations.

2.1.3. Logistics operations and their performance attributes

Success of several industries are attributed to the performance of

logistics operations (Knemeyer and Murphy, 2004); which would be

defined as individual or integrated services in transportation, ware-

housing, materials management, order management, customer ser-

vices and procurement(Robeson and Copacino, 1994; Skjoett-Larsen,

2000). Reduction of costs while providing the quality and schedule to

satisfy the customers are considered to be the major objectives of

operational performance (Lynch, 2000). Studies on supplier selection

analyse thesecriteriain general (Bevilacqua and Petroni, 2002; Chang

et al., 2006; Demirtas and Ustun, 2008) or detail as an inter-operation

mix classified by business (Lai et al., 2004), by processes (Robertson

et al., 2002; Tyan et al., 2003) or by decision variables (Liu and Ma,

2005; Jharkharia and Shankar, 2007).

Principal components of cost reduction in transportation are

ascribed by vehicle allocation and routing (Ross et al., 2007). The

three measures which are independent of the industry are the size

of fleet capacity (Tarantilis and Kiranoudis, 2001; Tarantilis et al.,

2004; Hsieh and Tien, 2004), distance (Di Benedetto, 1999; Chen

et al., 2005) and the driver force (Zhao and Stank, 2003; Di

Benedetto, 1999). Thequality in transportation is helping to realise

the delivery commitments in quality and time by avoiding the loss

ofgoods(Bowersoxet al., 1999; Panazzo et al., 1999) and relocation

(Leung et al., 2002; Powell and Topaloglu, 2003).

Warehouse performance involves physical infrastructure and

monitoring receipt, storage and movements of goods between the

distribution stations. Service quality for these activities relies on

forecast success (Van der Vorst et al., 1998) and layout flexibility

(Barad and Sapir, 2003); however, cost and timing is influenced by

regularity of receiving the goods (Hameri and Paatela, 1995;

Wegelius-Lehtonen, 2001; Lutz et al., 2003; Singh et al., 2005),distribution rates (Mason et al., 2003; Chen et al., 2005) and return

rate of goods (Lu, 2000; Mahadeven et al., 2003).

Order rate and order cycle are indispensable measures in

logistics service sales (Boyson et al.,1999; Dong and Chen, 2005).

These two measures ensure the success of transportation and

warehouse management success as well as customer services

(Chen etal.,2005). Improvements in sales cost reduction is realised

by balancing the demand rates and order rates ( Van Norden and

Van de Velde, 2005). Customer relations management also con-

tributed with newmeasureslike changes in customer portfolio and

complaint rates (Collins et al., 2001; Wouters and Sportel, 2005).

Realisation of resource planscannot be completedwithoutclose

monitoring of fulfilment and procurement (Gunasekaran and Ngai,

2003). Effective implementations of these activities result in

G. Kayakutlu, G. Buyukozkan / Int. J. Production Economics 131 (2011) 441–452 443



reduction of distribution costs and lead-time (Mahadeven et al.,

2003; Muffatto and Payaro, 2004). When procurement is consid-

ered, cost fluctuations and unusual demands are issues that cannot

be ignored in unstable economies (Aktas- and Ulengin, 2005).

Hamdan and Rogers (2008) gets into the details of operational

performance indicators under demand and finance constraints.

2.2. Suggested conceptual framework

The proposed conceptual framework is given in Fig. 1. Four

dimensions are defined in the framework: company performance

targets supporting the competitive success in a value chain are

planning activities, logistics operations and performance attributes

of logistics operations.

The first dimension will follow the literature grouping on

performance targets, consisting of networking, capital balancing

and customer focus. Specific to 3PL companies networking and

customer focus will be restricted. As networking with consumersis

not yet wide-spread among 3PL companies (Hertz and Alfredsson,

2003), alliance networking will be taken into account to indicate

business chains. On the other hand, long-term contracted custo-

mers are of strategical importance for 3PL logistics companies

(Bottani and Rizzi, 2006), while other customer related issues aretaken into account in operational performance.

The ‘‘planning activities’’ dimension includes strategic plans,

resource andinformationplansthat areneeded to be runin parallel

as an interface between the targets and the operations.

‘‘Logistics operations’’, the third dimension is summarised in

four groups based on accumulation of critical factors surveyed

previously. Transportation all alone includes several factors; but,

warehouse and inventory management are taken together; order

and customer management are combined into one, procurement

and fulfilment operations are represented by demand manage-

ment. Attributes of logistics operations are widely used by other

researchers as briefly summarised in Table 1b.

Transportation is expressed by the fleet capacity, distances

travelled, driver force, loss of goods and relocation rate. Ware-

house/Inventory measures are forecast reliability, receiving reg-

ularity; return rate; distribution rate and layout flexibility.

Attributes of order/customer management are interest on order

rate, order cycle consistency, complaint rates, request trends and

fluctuations in the customer portfolio (change). Demand coordina-

tion represents the procurement and fulfilment factors, which are,

fulfil rate, procurement efficiency, lead time effectiveness, demand

trends and cost fluctuation rate.

The detailed model used to evaluate the 3PL company perfor-

mance attributes is given in Fig. 2.

Logistics operations

Performance attributes of logistics operations

Planning Activities

Achieve

Competitive Success

in a Value ChainLogistics firm’s

performance targets

Fig. 1. Graphical representation of proposed evaluation framework.

Table 1b

Performance attributes of logistics operations.

Logistics

operation

Performance attribute Brief definition

Transportation Performance analysis of transportation activities.

Fleet Optimal fleet c apa city represen ting the number of v ehicles use d in logistics (Wu, 2009).

Distance travelled Optimum distance travelled by transportation fleet (Levinson, 2003)

Driver force Efficiency of the vehicle drivers (Anderson et al., 1998)

Los s of go ods Minimu m loss of goods dur ing the deliver y of the goo ds (Anderson et al., 1998)

Relocatio n rate Rate o f chan ge in the given addr ess of deliver y (Skjoett-Larsen, 2000)

Warehouse/

inventory

management

Performance analysis of warehouse management and inventory control activities

Forecast reliability How well the inventory amounts and security stocks are forecasted (Korpela et al., 2002)

Receiving regularity Regularity in the periodicity of receiving purchased material (Korpela et al., 2002). These two will affect the

inventory turnover rate

Return rate Product return rate over the quantity sold that will increase the inventory (Brito and Dekker, 2003)

Distr ibu tion rate Optimal pro du ct flow in one wareh ous e (Chen et al., 2005)

Layout flexibility Tolerance limits in assigning multiple warehouses to multiple locations (Barad and Sapir, 2003)

Order/customer

management

Order management activities and customer relations management related to orders

Interest/order rate Interest on order rate that will effect the prediction of order rates (Chen et al., 2005)

Order cycle consistency Consistency in repeating orders in a periodic cycle (Chen et al., 2005)

Customer complaint rate Number of customer complaint as a rate of received order amount (Marasco, 2008)

Request Trends The rate of match among the customer requests and the company future predictions (Marasco, 2008)

Change in customer portfolio Change in the customer portfolio profile that will force changes in requests (Collins et al., 2001)

Demand

coordination

Demand forecasting, updating, modifications according to changes in activities

Fulfil ra te Demand fu lfilment rate wh ich will satis fy th e customer (Stadtler, 2005)

Procure efficiency Efficiency rate of procurement which influences demand fulfil rate (Muffatto and Payaro, 2004)

Lead time effective Weight of lead time in demand satisfaction which changes by industry (Bogataj and Bogataj, 2004)

Demand trends Prediction robustness for future changes in demand (Landeghem and Vanmaele, 2002)

Cost fluctuation rate The rate of changes in demand fulfilment costs (Ross et al., 2007)




3. Research methodology: the analytic network process

Selection of a suitablemethodology that can decode the high-level

relationship model presented in Fig. 1 in order to determine the

importance of each component is a critical issue. This methodology

should be ableto use quantitative, qualitative, tangible and intangible

factors pertaining to the decision of which success attributes should

be evaluated. ANP is a unique method capable of taking the multiple

dimensions of information into the analysis.

ANP (Saaty, 1996) is a general form of the analytical hierarchy

process(AHP) first introducedby Saaty(1980). While theAHP employs

a unidirectional hierarchical relationship among decision levels, the

ANP enables interrelationships among the decision levels and attri-

butes in a more general form(Saaty and Takizawa, 1986; Saaty, 1996;

Saaty and Vargas, 1998). This provides a more accurate approach for

modelling complex decision/evaluation environment and then the

numberof ANP relatedworks has increased in the recent years (Meade

and Sarkis, 1998, 1999; Yurdakul, 2003; Sarkis, 2003; Chung et al.,

2005; Kengpol and Tuominen, 2006; Bayazit and Karpak, 2007; Jharkharia and Shankar, 2007; Ravi et al., 2008; Wadhwa et al., 2009).

The detailed model used to evaluate the 3PL company perfor-

mance attributes is given in Fig. 2.

The ANP uses ratio scale measurements based on pair-wise

comparisons; however, it does not impose a strict hierarchical

structure as in AHP, and models a decision problem using a

systems-with-feedback approach. The ANP then refers to the

systems of which a level may both dominate and be dominated,

directly or indirectly, by other decision attributes and levels. The

ANP approach is capable of handling interdependence among

elements by obtaining the composite weights through the devel-

opment of a ‘‘supermatrix’’. Saaty (1996) explains the supermatrix

concept similar to the Markov chain process. The supermatrix

development is defined in the next section.

The supermatrix development for ANP process is defined into

six major steps which are stated below:

Step 1. Develop an evaluation network hierarchy showing the

relationships among the criteria analysed. The hierarchy has to

show the goal, the targets, the cluster of factors and the

attributes.

Step 2. Elicit pair-wise comparisons among the factors influencing the

evaluation. Eliciting preferences of various components and attri-

butes will require a series of pair-wise comparisons where the

assessorwill comparetwocomponents ata timewithrespect toan

upper level ‘‘control’’ criterion. In ANP, pair-wise comparisons of

the elements in each level are conducted with respect to their

relative importance towards their control criterion. Saaty has

suggested a scale of 1–9 when comparing the two components,

with a score of 1 representing indifference between the two

components and 9 being overwhelming dominance of the com-

ponent under consideration over the comparison component.When scoring is conducted for a pair, a reciprocal value is

automatically assigned to the reverse comparison within the

matrix. Since many of these values have strategic importance,

strategic group decision-making tool is used.

Step 3. Calculate relative-importance-weight vectors of the factors.

Once all the pair-wise comparisons are completed, the relative

importance weight for each component is determined. The

weights can be determined as the largest eigenvalue.

Step 4. Form a supermatrix (i.e. a two-dimensional matrix com-

posed from the relative-importance-weight vectors) and normalise

this supermatrix, so that the numbers in every column sum to one.

The priority vectors for each pair-wise comparison matrix will

be needed to complete the various supermatrix submatrices.

The priority vectors are needed to complete the supermatrix, a

Performance

Attributes

Planning Activities

Achieving global

competition success

in the logistics

market

Logistics Operations

GOALAlliance Network

(ALNW)

Capital Balancing

(CBAL)

Accredited Customers

(ACCU)

(A)(B) (C)

(D)

Strategies (STRA) Resources (RSCS) Information (INFO)

Forecast Reliability (FR)

Receiving Regularity(RS)

Return Rate (RR)

Distribution Rate (DR)

Layout Flexibility(LF)

Interest/Order Rate (OR)

Order Cycle Consist. (OC)

Complaint Rates (CR)

Request Trends (RT)

Change in Portfolio (CP)

Performance Targets

(E)

Transportation Mgmt

(TRANS)

WH/Inventory Mgmt

(WHINV)

Order-Customer Mgmt

(ORD/CUST)

Demand Coordination

(DEMM)

Fleet (FL)

Distance (DS)

Driver force (DF)

Loss of goods (LG)

Relocation rate (RL)

Fulfil Rate (FlR)

Procure Efficiency (PE)

Lead Time Effective(LE)

Demand Trends (DT)

Cost Fluct. Rate (CS)

Fig. 2. Evaluation network for achieving global competition in the logistics market.




partitioned matrix allowing the resolution of the effects of the

interdependence that exists between the clusters within the

evaluation network hierarchy. Each submatrix is composed of a

set of relationships between two clusters.

Step 5. Calculate converged (‘‘stable’’) weights from the normalised

supermatrix. S upermatrix evaluation is to determine the final

relative importance weights of each of the attribute levels to

help guarantee convergence. The columns of the supermatrix

must be ‘‘column stochastic’’, i.e. the sum of weights of eachcolumnfor thesupermatrixmust be equal to 1. To complete this

task, each column is to be normalised by dividing weight in the

columnby thesum of that column. Forconvergenceto a final set

of weights, the normalised (column stochastic) supermatrix is

raised to the power 2k+1 where k is an arbitrarily large number

until stabilisation (values in the supermatrix do not change

when it is multiplied by itself, converged).

Step 6. Determine overall weightings of evaluation attributes. The

last step in the ANP process is to take the final results of

the converged supermatrix and the eigenvector values from the

earlier pair-wise comparisons and calculate the relative impor-

tance weight for success attribute of each level. Once all the

relative weights have been calculated, a composite (global)

weight for each success attribute is determined. This is accom-

plished by aggregating theweights. Theresultis a single weight

value for success attributes of each level.

These six steps will be discussed in conjunction with the case

study in the following section.

4. Application of the proposed framework

The proposed analytic framework is applied in two major 3PL

companies of South East Europe of Turkish origin. Company A and

company E are chosen since they have similar volume of logistics

business with different background and strategies. Though, they

both take an important role in the value chain through Europe.

Company A has been in inbound transportation business forthe

last twenty years, whilst giving customs execution and insurance

services. It has been international for the last ten years and started

the warehouse management services with three warehouses in

differentregionsof Turkeyonlya fewyearsago.Current portfolio of

five hundred customers is served by three hundred and fifty

personnel. Integrated services of distribution, warehousing and

sales channel management are given for beverage and brewery

companies. Distribution of fuel oil is one of the major services that

should be accounted. The company has invested in information

technology since the day of establishment but the implementation

of integrated information system solutions is in process currently.

Company E is only established in 1990 as a transportation

company but quickly switched into an integrated logistics solu-

tions company in 1994. This company owns several warehouses indifferent regions of South East Europe including the biggest

technology rich textile logistics centre of the region. Integrated

services are only given to the accredited customers; hence, there

are only ten international conglomerates like 3M, Marks & Spencer

and Metro Group in the customer base. Since the starting day

integrated information systems, networking and automatic data

collection and processing is given special interest and the ware-

houses are technically equipped.

The proposed framework is applied in both companies to observe

the similarities and differences in business performance measures.

Assessors from both companies are high level managers and one

business consultant of each. General Managers of both companies

have graduate degrees of education and have experiences in global

manufacturing companies. Interviewed consultants are experienced

in industry for at least ten years. Analysis is performed on average

results of managers and consultants of each company.

4.1. The evaluation network hierarchy

According to Fig. 2 the proposedlogistics systemis evaluatedon

four different dimensions (levels or clusters); Planning Activities,

Performance Targets, Logistics Operations and Performance attri-

butes of logistics operations. In this study, the interdependence orfeedback type relationship occurs between planning activities and

performance targets as represented by two reverse arrows among

those levels. The other arrows in the model indicate a one-way

relationship. In addition, the interdependency relationships of

logistics operations are shown by a looped arc in Fig. 2. The capital

letters from A to E in parenthesis in Fig. 2 represent the weight

matrices used in the supermatrix construction which is described

in details in Section 4.4.

4.2. Pair-wise comparisons

Eliciting preferences of various components and attributes will

require a series of pair-wise comparisons where the assessor will

compare two components at a time with respect to an upper level‘‘control’’ criterion. In ANP, like AHP, pair-wise comparisons of the

elements in each level are conducted with respect to their relative

importance towards their control criterion (Saaty, 1996).

Saaty has suggested a scale of 1–9 when comparing the two

components, with a score of 1 representing indifference between the

two components and 9 being overwhelming dominance of the

component under consideration (row component) over the compar-

ison component (column component). If a component has a weaker

impact onthecontrol criterion, therangeofscoreswill befrom 1 to 1/9,

where 1 represents indifference and 1/9 an overwhelming dominance

by a columnelement over therow element. When scoring is conducted

for a pair, a reciprocal value is automatically assigned to the reverse

comparison withinthematrix. That is,if aij is a matrixvalue assigned to

the relationshipof component i to component j, then a ji is equal to1/aij

(or aijna ji¼1). Since many of these values have strategic importance,

strategic group decision-making tool, Delphi approach (Delbecq et al.,

1975; Melnyk et al., 2008) is used to assign meaningful values to the

pair-wise comparisons. Expertise of the assessors chosen has avoided

the issues caused by difficulty of evaluation.

Experts were asked questions such as: ‘‘In terms of the goal of

globalcompetitiveness what is the relative importance of informa-

tion planning compared to the strategies planning?’’ In this

example, the decision maker viewed collection as ‘‘slightly more

important’’ by the score of 3.000 (as shown in the cell at the

intersection of the strategies row and the information column in

Table 2a). Reciprocally, the intersectionof the information rowand

strategies column shows a score of 1/3. This pair-wise comparison

approach is used to populate the matrix.

4.3. Calculation of relative importance weights

Once all the pair-wise comparisons are completed, the relative

importance weight for each component is determined. The priority

Table 2a

Pair-wise comparison matrix of Company A importance of planning elements

relative to the goal (A).

GOAL Strategies Resources Information Weights

Strategies 1 5 3 0.64

Resources 1/5 1 1/3 0.10

Information 1/3 3 1 0.26




vector shows that for this study, the strategic planning activities

were given the highest rating (0.64 and 0.65) (the weighted

priorities are shown as the last column in Table 2). Given that A

is thepair-wise comparison matrix; theweights canbe determined

by expression:

Aw ¼ lmaxw ð1Þ

where lmax is the largest eigenvalue of A. Saaty provides several

algorithms for approximating w. In this paper a two-stage algorithmwas used to involve forming a new nÂn matrix by dividing each

element in a column by the sum of the column elements and then

summing theelementsin each rowof theresultant matrixand dividing

bythe n elements in therow.This is referred as theprocess ofaveraging

over normalised columns. The procedure may be algebraically repre-

sented as

wi ¼

P J j ¼ 1ðaij=

PI k ¼ 1 akjÞ

J for i ¼ 1,2,::,I ð2Þ

where wi is the weighted priority for component I , J is number of

columns (components), and I is number of rows (components).In the assessment process there may occur a problem in the

transitivity or consistency of the pair-wise comparisons. For an

explanation on inconsistencies in relationships and their calcula-

tions see Saaty (1980). The priority vectors for each pair-wise

comparison matrix will be needed to complete the various super-

matrix submatrices. We will need a total of 19 priority vectors to

complete our supermatrix. This requirement means that 19 pair-

wise comparison matrices must be completed. The pair-wise

comparison matrix’ results were used below after tests and

validation of the consistency.

4.4. Supermatrix formation

ANP uses supermatrix to allow the resolution of interdepen-

dence that existsbetween thelevels andelements of theevaluation

network hierarchy. The supermatrix is a partitioned matrix, where

each submatrix is composed of a set of relationships between two

clusters in the graphical model. A generic supermatrix is shown in

Fig. 3, with the notation representing the various relationships

from Fig. 2; for instance, ‘‘A’’ is the submatrix representing the

influence relationship between the Planning Activities and control

factor of achieving global competition success by determining the

Table 2b

Pair-wise comparison matrix of Company E importance of planning elements

relative to the goal (A).

GOAL Strategies Resources Information Weights

Strategies 1 5 3 0.65

Resources 1/5 1 3 0.22

Information 1/3 1/3 1 0.13

Goal Perform.

Targets

Planning Operations

Goal 0

Performance Targets 0 C 0

Planning A 0

Operations 0 D

0 0

0

B 0

0 E

Fig. 3. General submatrix notation for supermatrix.

Table 3a

Initial supermatrix M for determining the weights of logistics operations’ performance attributes for Company A.

GOAL Perform. Targets Planning Operations

ALNW CBAL ACCU STRA RSCS INFO TRANS WHINV ORD/CUST DEMM

GOAL 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Performance targets ALNW 0.00 0.00 0.00 0.00 0.10 0.65 0.10 0.00 0.00 0.00 0.00

CBAL 0.00 0.00 0.00 0.00 0.26 0.22 0.64 0.00 0.00 0.00 0.00

ACCU 0.00 0.00 0.00 0.00 0.64 0.13 0.26 0.00 0.00 0.00 0.00

Planning STRA 0.64 0.64 0.10 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00

RSCS 0.10 0.26 0.64 0.26 0.00 0.00 0.00 0.00 0.00 0.00 0.00

INFO 0.26 0.10 0.26 0.64 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Operations TRANS 0.00 0.00 0.00 0.00 0.08 0.55 0.08 0.60 0.21 0.26 0.26

WH/INV 0.00 0.00 0.00 0.00 0.52 0.23 0.52 0.22 0.06 0.52 0.52

ORD/CUST 0.00 0.00 0.00 0.00 0.20 0.11 0.20 0.10 0.60 0.10 0.12

DEMM 0.00 0.00 0.00 0.00 0.20 0.11 0.20 0.08 0.13 0.12 0.10

Table 3b

Initial supermatrix M for determining the weights of logistics operations’ performance attributes for Company E.

GOAL Perform. targets Planning Operations


GOAL 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00


CBAL 0.00 0.00 0.00 0.00 0.64 0.26 0.10 0.00 0.00 0.00 0.00

ACCU 0.00 0.00 0.00 0.00 0.26 0.10 0.64 0.00 0.00 0.00 0.00

Planning STRA 0.65 0.26 0.65 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00

RSCS 0.22 0.64 0.22 0.64 0.00 0.00 0.00 0.00 0.00 0.00 0.00

INFO 0.13 0.10 0.13 0.26 0.00 0.00 0.00 0.00 0.00 0.00 0.00


WH/INV 0.00 0.00 0.00 0.00 0.12 0.09 0.15 0.08 0.06 0.23 0.23

ORD/CUST 0.00 0.00 0.00 0.00 0.56 0.57 0.39 0.55 0.51 0.07 0.07

DEMM 0.00 0.00 0.00 0.00 0.26 0.27 0.39 0.31 0.29 0.12 0.12




weights of performance attributes of logistics operations. Tables 3a

and 3b are the detailed initial supermatrix of the proposed model.

4.5. Calculation of stable weights from the normalised supermatrix

The next step with the supermatrix evaluation is to determine

the final relative importance weights for each operation. To

complete this step andto help guaranteeconvergence, thecolumns

of the supermatrix must be ‘‘column stochastic’’and thus minimisethe possibility for divergence to infinity or convergence to zero.

That is, the sum of weights of each column for the supermatrix

must be equal to 1. In order to achieve this sum, each column is

normalised by dividing weight in the column by the sum of that

column.

Let wij be any weight in the jth column then normalised weight

can be expressed as (3)

wN ij ¼

wijP J j ¼ 1

wij

or 0 whereX J

j ¼ 1wN

ij ¼ 1 ð3Þ

For convergence to a final set of weights, we raise the normal-

ised (column stochastic) supermatrix to the power 2k+1 where k is

an arbitrarily large number, increased until stabilisation of the

weights occurs (i.e. the point where values in the supermatrix donot change as a result of multiplication by itself, is also defined as

convergence). For our example, convergence occurred when the

supermatrix was raised to the 39th power. The long-term stable

weighted values to be used in the analysis are shown in the

converged supermatrix given in Tables 4a and 4b. The results show

the most important operation is transportation for both of the

companies (0.37; 0.34). For Company A the following operation is

warehouse/inventory management, whereas for Company E it is

the order/customer management.

4.6. Final relative importance weight calculation

The sixth and last step in the ANP process is to take the final

results of the converged supermatrix and the eigenvector values

from the earlier pair-wise comparisons and calculate the relative

importance weight for each operational performance attribute. To

analyse operational performance attributes, pair-wise comparison

similar to the one that is done in Step 2 is realised to achieve

importance weights (or eigenvectors). There are three separatepair-wise comparison matrices that have to be developed for this

step in the analysis. Table 5(a–c) gives the evaluation results for

Company A and Table 6(a–c) gives results for Company E.

Once all the relative weights have been calculated, a composite

(global) weight for each performance attribute is determined. This

is accomplished by aggregating the weights. The result is a single

weightvalue for performance attributes of logistics operations. The

combination of all the weights is given in Tables 7 and 8. Company

A appears to have Fleet (0.1850), Forecast Reliability (0.1428),

Interest/Order Rate (0.1224) and the Driver Force (0.1036) as

performance attributes that have the most impact on the success

of global competitiveness. Whereas Company E has Fleet (0.1734),

Complaint rates (0.1320), Lead Time Effectiveness (0.0945), the

Driver Force (0.0884) and Change in Customer Portfolio (0.0810) asperformance attributes that have the most impacton thesuccess of

global competitiveness.

The Fleet being the most important performance attribute was

not a surprise for any assessor. The reasons behind that may be the

cultural issue of insisting to own the trucks as explained by Aktas-

and Ulengin (2005). Forecast reliability seems to be solved by

technology in Company E butnot yetin company A. Thedriver force

is an attribute, most possibly depending on the loweducation level

of drivers.

Table 4a

Converged supermatrix at M39 for Company A.



GOAL 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00


CBAL 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

ACCU 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Planning STRA 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

RSCS 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

INFO 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00


WH/INV 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28

ORD/CUST 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24

DEMM 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11

Table 4b

Converged supermatrix at M39 for Company E.



GOAL 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00


CBAL 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

ACCU 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Planning STRA 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

RSCS 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

INFO 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00


WH/INV 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15

ORD/CUST 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30

DEMM 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21




Table 5

Relative importance of the logistics operations’ performance attributes for Company A.

(a) The relative importance of the transportation management performance attributes

FL DS DF LG RL

Fleet 1 5 7 3 3Distance 1/5 1 1/9 1/3 1/5Driver force 1/7 9 1 5 7Loss in goods 1/3 3 1/5 1 1/5

Relocation rate 1/3 5 1/7 5 1

(b) The relative importance of the warehouse/inventory management performance attributes

FR RS RR DR LF

Forecast reliability 1 3 7 9 5Receiving regularity 1/3 1 5 7 3Return rate 1/7 1/5 1 1/5 1/3Distribution rate 1/9 1/7 5 1 3Layout flexibility 1/5 1/3 3 1/3 1

(c) The relative importance of the order/customer management performance attributes

OR OC CR RT CP

Interest/order rate 1 3 7 9 5Order cycle consist. 1/3 1 5 7 3Complaint rates 1/7 1/5 1 3 1/3Request trends 1/9 1/7 1/3 1 1/5

Change in portfolio 1/5 1/3 3 5 1(d) The relative importance of the demand coordination performance attributes

FlR PE LE DT CS

Fullfil rate 1 7 5 1/3 3Procure efficiency 1/7 1 1/3 1/9 1/5Lead time effectiveness 1/5 3 1 1/7 1/3Demand trends 3 9 7 1 5Cost fluctuation rate 1/3 5 3 1/3 1

Table 6

Relative importance of the logistics operations’ performance attributes for Company E.

(a) The relative importance of the transportation management performance attributes

FL DS DF LG RL

Fleet 1 9 3 5 7Distance 1/9 1 1/7 1/5 1/3Driver force 1/3 7 1 3 5Loss in goods 1/5 5 1/3 1 3Relocation rate 1/7 3 1/5 1/3 1

(b) The relative importance of the warehouse/inventory management performance attributes

FR RS RR DR LF

Forecast reliability 1 3 5 3 7Receiving regularity 1/3 1 7 3 5Return rate 1/5 1/7 1 1/3 5

Distribution rate 1/3 1/3 3 1 5

Layout flexibility 1/7 1/5 1/5 1/5 1

(c) The relative importance of the order/customer management performance attributes

OR OC CR RT CP

Interest/order rate 1 1/3 1/5 1/3 1/5

Order cycle consist. 3 1 1/3 1/5 1/5

Complaint rates 5 3 1 5 3

Request trends 3 5 1/5 1 1/3

Change in portfolio 5 5 1/3 3 1

(d) The relative importance of the demand coordination performance attributes

FlR PE LE DT CS

Fullfill rate 1 1/3 1/5 1/5 1/3

Procure efficiency 3 1 1/3 1/5 1/3

Lead time effectiveness 5 3 1 5 3

Demand trends 5 5 1/5 1 1/3

Cost fluctuation rate 3 3 1/3 3 1




5. Concluding remarks and future directions

This research aims to contribute both academic studies and the

3PL logistics management in reengineering the strategies in a value

chain. The study addresses the need for defining new performance

factors to develop competitive success. A framework is developed in

order toidentify andrank thepossible factors.The poolof criteriato be

considered by a 3PLcompanyis constructedon a literature survey and

refined with industrial experts. The model considers tangible, intan-

gible, quantitative, qualitative factors in the analytic evaluation.

As shown in Table 1, the previous studies were focused on

integrating either strategic performance factors or bringing

together the attributes in one or more operations. This study willcontribute to thelogistics research literature with theunique effort

to amalgamate strategic factors and operational factors through

planning activities.

The fact that performance issues are interdependent is clearly

observed in the background section. Research on strategic deci-

sions to develop competitive success uses alternate decision-

makingtools. Thefact that factors influencing the strategies cannot

be mutually excluded, ANP has become a unique method. The ANP

method is used in this study to offer a more precise and accurate

analysis by integrating interdependent relationships, though it

requires more time and effort (additional interdependency rela-

tionships increase geometrically the number of pair-wise compar-

ison matrices).

Thecase is run on two 3PL companies providing integrated services

in international competition with each other.The aimof choosing these

two companies was to observe the differences in importance of

performance targets, planning and operational performance factors

in conjunction with (i) the level of information technology utilisation

and (ii) the richness of logistics services provided.

The results show that both give the biggest importance to

strategic planning and transportation operation; but the differ-

ences areobservedin thefollowers. This is possible mainly because

both companies started in transportation business and evolved in

additional logistics operations. The Fleet and the Driver Force both

taking place among the important operational performance attri-

butes fortifies the possibility. Issues in the chosen factors cannotbe

removed by information technologies but control and monitor

operations and by learning organisation.

The second choices are quite different. Company A shows the

need for technology by the choice of planning and operational

factors like forecast reliability and interest/order rate, which are

known to be handled by integrated information systems. Company

E on the other hand, shows interest in customer relations manage-

ment willing to measure complaint rates, change in portfolio and

lead time effectiveness.

The proposed framework has a few limitations as well. The

results dependon theinitial responses. Even if theeffort is made by

choosing the assessors as managers and the business consultants,

the possibility of bias cannot be totally removed. Hence, this study

will be extendedto take theevaluation of customers into account to

reduce the bias by opposition.

Development of the model can also be continued by applying

sensitivity analysis. Comparing the results of this study and theapplication of another analytical method will provide more defi-

nitiveconclusions, which can lead forthe developmentof an expert

system in performance evaluation.

Acknowledgements

The authors acknowledge the managers and consultants of

Adahan Logistics and Ekol Logistics for their unlimited support

in evaluation of the framework. The authors thank the Editor,

Professor T.C. Edwin Cheng, and anonymous reviewers for their

valuable comments and suggestions to improve the early version

of the paper.

Table 7

Composite priority weights for logistics operations’ performance attributes of

Company A.

Logistics

operations

Local

weights

Performance

attributes

Local

weights

Global

weights

Transportation

management

0.37 Fleet 0.50 0.1850

Distance 0.03 0.0111

Driver force 0.28 0.1036

Loss in goods 0.06 0.0222Relocatio n r ate 0.13 0.048 1

Warehousing/

inventory

management

0.28 Forecast

reliability

0.51 0.1428

Receiving

regularity

0.28 0.0784

Return rate 0.04 0.0112

Distribution rate 0.10 0.0280

Layout flexibility 0.07 0.0196

Order/customer

management

0.24 Interest/order

rate

0.51 0.1224

Order cycle

consist.

0.26 0.0624

Complaint rates 0.07 0.016 8

Request trends 0.03 0.0072

Change in

portfolio

0.13 0.0312

Demand

management

0.11 Fullfill rate 0.26 0.0286

Procure efficiency 0.03 0.0033

Lead time

effectiveness

0.06 0.0066

Demand trends 0.51 0.0561

Cost fluctuation

rate

0.14 0.0154

Table 8

Composite priority weights for logistics operations’ performance attributes of

Company E.

Logistics

operations

Local

weights

Performance

attributes

Local

weights

Global

weights

Transportationmanagement

0.34 Fleet 0.51 0.1734Distance 0.04 0.0136

Driver force 0.26 0.0884

Loss in goods 0.13 0.0442

Relocation rate 0.06 0.0204

Warehousing/

inventory

management

0.15 Forecast

reliability

0.44 0.0660

Receiving

regularity

0.29 0.0435

Return rate 0.08 0.0120

Distribution rate 0.15 0.0225

Layout flexibility 0.04 0.0060

Order/customer

management

0.30 Interest/order

rate

0.05 0.0150

Order cycle

consist.

0.08 0.0240

Complaint rates 0.44 0.1320Request trends 0.16 0.0480

Change in

portfolio

0.27 0.0810

Demand

management

0.21 Fullfill rate 0.05 0.0105

Procure efficiency 0.09 0.0189

Lead time

effectiveness

0.45 0.0945

Demand trends 0.18 0.0378

Cost fluctuation

rate

0.23 0.0483




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