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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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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)
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(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
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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)
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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.
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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
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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
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 0.64 0.26 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
Operations TRANS 0.00 0.00 0.00 0.00 0.06 0.07 0.07 0.06 0.14 0.58 0.58
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
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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 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 0.00 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
Operations TRANS 0.37 0.37 0.37 0.37 0.37 0.37 0.37 0.37 0.37 0.37 0.37
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 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 0.00 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
Operations TRANS 0.34 0.34 0.34 0.34 0.34 0.34 0.34 0.34 0.34 0.34 0.34
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
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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
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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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