Benchmarking Discussion
KEY TERM IS: BENCHMARKING
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Production Planning & Control
The Management of Operations
ISSN: 0953-7287 (Print) 1366-5871 (Online) Journal homepage: https://www.tandfonline.com/loi/tppc20
An empirical assessment of the operational
performance through internal benchmarking: a
case of a global logistics firm
Arijit Bhattacharya & Dhyan Albert David
To cite this article: Arijit Bhattacharya & Dhyan Albert David (2018) An empirical assessment
of the operational performance through internal benchmarking: a case of a global logistics firm,
Production Planning & Control, 29:7, 614-631, DOI: 10.1080/09537287.2018.1457809
To link to this article: https://doi.org/10.1080/09537287.2018.1457809
Published online: 04 Apr 2018.
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Production Planning & control, 2018
Vol. 29, no. 7, 614–631
https://doi.org/10.1080/09537287.2018.1457809
An empirical assessment of the operational performance through internal
benchmarking: a case of a global logistics firm
Arijit Bhattacharyaa and Dhyan Albert Davidb
anorwich Business School, university of East anglia, norwich, uK; bHilti Emirates, dubai investment Park, dubai, united arab Emirates
ABSTRACT
This article solves an operational performance measurement problem of a global logistics firm through an
internal benchmarking tool. The intended impact is to enable logistics firms to form a deeper understanding
of their own internal processes and metrics. The methodology of this in-depth action research involves
a sequential approach with a series of interviews, questionnaire-based surveys, operations data collated
through observations and process mapping yielding real-world data. A series of statistical tests are
conducted to analyse the collated data. Strategic priorities of the firm are integrated with the firm’s
operational performance to ascertain the effective performance by considering both the tangible and
intangible measures. The outcomes inform both practitioners and academics how the firm could improve
its freight forwarding business’s profitability by ensuring that its operations meet the prioritised criteria.
The ‘best practice’ derived from internal benchmarking forms an intermediate step towards external
benchmarking. The outcomes facilitate investigating the current business strategy, the standard operating
procedures and the scope of improving those.
This article contributes towards the development of an internal
benchmarking tool to measure the effectiveness of the opera-
tional performance of each department of a global logistics firm.
Today’s companies are driven by the need to shorten business
cycles and improve quality while simultaneously containing
operating costs; hence, company management requires more
than simply reports upon historic data. Rather, it needs to have
better operating information and greater insight into what can
support and sustain the organisation in the foreseeable future.
As the logistics industry is endeavouring to develop real-time
information systems (Ahmad and Mehmood 2016) to improve
performance (Lu and Yang 2010), it is essential to benchmark the
performance (Andersen and Jordan 1998) of logistics operations
with the objective of identifying the best practices and their
implementation, together with formulating strategies, tech-
niques and technologies for enhanced organisational respon-
siveness and competitiveness (Gunasekaran 2002).
The objective of benchmarking is to identify and understand
the best practices from the case of a global logistics firm. A ‘best
practice’ is, simply, the best way to execute a process; it is deemed
one of today’s most effective business strategies, currently deliv-
ering results for organisations of all sizes and in all industries. In
particular, it has the potential to propel quantum improvement
in internal auditing (Julien 1993). Therefore, benchmarking could
introduce the notion of continuous improvement in a concrete
and positive way in assessing operational performance. It can
identify paths for innovation in a firm’s processes, activities and
attitudes (Spendolini 1994).
This article contributes to the literature by pinpointing the
gaps that have developed over time in the standard operating
procedures (SOP) and policies of a specific global firm’s operations
compared to today’s industry requirements. Identification of the
knowledge gaps and appropriate recommendations are used to
improve the performance of the firm’s operations. In particular,
the priorities and requirements of the firm’s shipping profession-
als are determined, and these are used to shape the firm’s prod-
uct offerings to thereby meet their customers’ needs. The study’s
further implications relate to examining the firm’s usage of its
current business strategy and SOPs, and identifying the scope
for improving the same.
The aim of the research is to understand what the studied
global logistics firm could do to ensure profitability in the work-
ings of its freight forwarding business, and to identify if the
firm’s operations meet performance metrics. A paradigm shift is
reported in this article through implementing a novel, holistic,
internal benchmarking tool within the firm by exploring the fol-
lowing research questions:
• How can the operational performance of the depart-
ments in a global logistics firm be assessed, measured and
improved, prioritising the requirements of shipping profes-
sionals in the industry?
ARTICLE HISTORY
received 12 June 2017
accepted 26 February 2018
KEYWORDS
Freight forwarding industry;
operational performance;
internal benchmarking;
logistical strategies
© 2018 informa uK limited, trading as taylor & Francis group
CONTACT arijit Bhattacharya a.Bhattacharya@uea.ac.uk, arijit.bhattacharya2005@gmail.com
http://orcid.org/0000-0001-5698-297X
mailto:A.Bhattacharya@uea.ac.uk
mailto:arijit.bhattacharya2005@gmail.com
http://www.tandfonline.com
http://crossmark.crossref.org/dialog/?doi=10.1080/09537287.2018.1457809&domain=pdf
PRODUCTION PLANNING & CONTROL 615
2010). Every identified factor has to be measured and included
in the benchmarking tool (Kablan and Dweiri 2003), whether
a financial dimension or otherwise (Gunasekaran, Patel, and
Tirtiroglu 2001).
2.1. Internal benchmarking
‘Benchmarking’ is defined as the process of improving perfor-
mance by continuously identifying, understanding, analysing
and adapting the best practices or processes inside and outside
an organisation to gain and maintain up-to-date understanding
of the appropriate performance levels and drivers behind suc-
cess (Camp 1995; Kelessidis 2000; Zairi 1996). Benchmarking
tools have been successfully utilised by Xerox, Nissan/Infiniti, ICI
Fibers, Texaco, American Express, Kodak Rover, AT&T, Chevron
and 3 M to enhance their business success (Soni and Kodali
2010; Wong and Wong 2008).
The process of benchmarking provides ideas to a company,
enabling identification and implementation of the most effec-
tive solutions for realising breakthroughs in performance (Tutcher
1994). Benchmarking provides both motivation and learning in
performance improvements, as benchmarking team in the com-
pany compares all of its internal practices with the best practices
of the industry (Gunasekaran 2001; Hyland and Beckett 2002).
Feedback from benchmarking usually provides considerable
scope for improvements and suggests ways to imitate strategies
with the potential to achieve better operational performance.
Earlier studies of benchmarking in logistics have reported
types of performance or practice, including achievable perfor-
mance levels for comparison, setting performance targets and
possible benchmarking methods (van Hoek 2000). However, most
of the prior research relates mainly to benchmarking schemes
for companies whose logistics activities were not central to their
operations. Hanman (1997) and Gunasekaran (2002) employed
the leaders–laggers analysis to compare a firm’s performance to
best practice. Gilmour (1999) proposed a set of benchmark meas-
ures based on given collection of capabilities. Van Landeghem
and Persoons (2001) proposed a causal model as a means to iden-
tify possible
initiative
s to bridge the performance gap between
a company and the best-in-industry performers.
The majority of the research conducted in logistics bench-
marking is focused on performance appraisal, integration and
information systems through external benchmarking tool (Binder,
Clegg, and Egel-Hess 2006; Salem 2010; Southard and Parente
2007; Suzuki 2015). However, these studies do not focus on
the elements of enterprises’ internal competencies, which thus
• In what ways can an internal benchmarking tool contribute
to better operational performance of the global logistics
firm?
• In what ways does the firm’s multi-domestic strategy have
a major impact on the factors influencing the performance
of its freight forwarding business?
• What operational and strategic recommendations can the
devised internal benchmarking tool generate to enable the
firm to achieve better operational performance?
To address these research questions, a set of objectives are
framed. The first objective is to identify the priorities and
requirements of shipping professionals in the firm’s freight for-
warding business in the UAE. The second objective is to derive
the relative importance of the firm ‘s stakeholders (both external
and internal) through a weighted average framework, and to
measure the critical factors/priorities earlier identified and rated
by the organisation. The third objective is to provide an internal
benchmarking tool for the firm and render, thereby, appropri-
ate strategies for continuous improvement of their operational
performance.
The article is organised as follows. Section 2 provides the
operational details of the studied firm’s freight forwarding
departments. Section 3 then examines the study’s theoretical
foundations. The details regarding the research methodology
are presented in Section 4, followed by the results and analyses
in Section 5. Finally, in Section 6, the article concludes with rec-
ommendations of operational strategies, theoretical and practical
implications and the scope for further research.
Application of the benchmarking technique in logistics has
grown extensively in the last three decades (Dattakumar and
Jagadeesh 2003; Wong and Wong 2008). Benchmarking leads
to achieving improved operational performance (Francis 2008;
Voss, Åhlström, and Blackmon 1997). A literature review on per-
formance measurement in supply chain and logistics manage-
ment reveals that there have been relatively few attempts to
systematically collate measures for assessing the performance
of freight forwarding firms through internal benchmarking
(Table 1).
Anderson and McAdam (2004) envisaged benchmarking as
a possible means of achieving increased radical and innovative
transformation in enterprises. Financial performance is no longer
the key driver of benchmarking (Adebanjo, Abbas, and Mann
Table 1. literature on benchmarking and operational performance.
Literature Description
chung et al. (2015) compared the operational efficiency of major cargo airports through a benchmarking tool to examine various aspects of oper-
ational efficiency
Southard and Parente (2007) determined criteria for internal benchmarking and applied a qualitative benchmarking tool to internal processes
Binder, clegg, and Egel-Hess (2006) Proposed a benchmarking methodology and deployed it within a large and complex organisation to benchmark its ‘packing
and filling’ processes
Salem (2010) determined benchmarking criteria for manufacturing organisations, assessing their key capabilities and prioritising them
using an analytic hierarchy process
niemi and Huiskonen (2008) a stepwise benchmarking process was conducted to identify the best logistical practices and to implement them utilising an
internal benchmarking approach
amaral and Sousa (2009) developed a categorised list of barriers to internal benchmarking, validating them with the case of an internal benchmarking
initiative
616 A. BHATTACHARYA AND D. A. DAVID
represent a gap in the prior literature. Internal benchmarking pro-
vides the benefits of identifying, assessing, and transferring the
practices from a high-performing logistics company to another
similar organisation, using the best practices prevailing in logistics
companies as an intermediate step towards external benchmark-
ing (Soni and Kodali 2010).
There is a knowledge gap regarding the measurement of
logistics performance using internal benchmarking, which should
include financial and non-financial measures, including tangibles
and intangibles, as reaffirmed by Gunasekaran (2002). The direc-
tion of addressing benchmarking is no longer process-oriented;
rather, a holistic approach encompassing strategies where sys-
tems orientation is adopted (Yasin 2002). This indicates that inter-
nal benchmarking in logistics performance is required to affect
a paradigm shift in performance measurement techniques and
applications. Therefore, it is appropriate that discourse and dis-
cussion regarding logistics performance should give adequate
attention to benchmarking.
Overall, freight forwarding is essentially a logistical service-ori-
ented sector. Although a number of cases and studies on internal
benchmarking have reported on the manufacturing sector, many
of their results are not clearly implementable as these studies fail
to focus on the elements of enterprises’ internal competencies.
Further, there is a growing need to develop a methodology to
guide benchmarking in supply chain collaboration (Simatupang
and Sridharan 2004). This research aims to fill this knowledge
gap by focusing specifically on the operations and performance
measures most relevant to today’s freight forwarding industry.
2.2. Performance measures for the logistics industry
One of the most important issues in the logistics benchmarking
process is to define what performance measures are to be stud-
ied (Moffett, Anderson-Gillespie, and McAdam 2008). The cor-
rect metrics are critical elements to a company’s performance
(Wong and Wong 2008). A performance measure is construed
as a metric to quantify the efficiency and effectiveness of opera-
tions (Neely and Gregory 1995). Even today, most organisations
tend to benchmark based on ‘hard’ rather than ‘soft’ data (Cassell,
Nadin, and Gray 2001), ignoring non-financial measures, e.g.
quality, reliability, customer satisfaction, human resources and
other criteria, including learning (Geanuracos 1994). It is, thus,
imperative that performance measurement should be based on
not only quantitative data but also qualitative data that help to
improve performance at all managerial levels.
There have been relatively few attempts to systematically col-
late measures for evaluating the performance of freight forward-
ing organisations (Chung et al. 2015). Industry experts perceive
that cost, quality and efficiency are the most important criteria
(Lockamy and McCormack 2004; Wie 2014). Concurrent commit-
ment to both quality and supply chain improvement has been
found to have the greatest effect on performance (Tan 2001).
Emphasis on the measurement of cost, time, quality, flexibility and
innovativeness is required (Shepherd and Günter 2006). Customer
service performance of ocean freight forwarding industries can
be enhanced through the industries’ innovation capability (Yang
2012).
A performance measurement system can be internally compa-
rable if trade-offs among disparate performance criteria are made
(Caplice and Sheffi 1995). However, on some levels, it is impossible
to assign measures neatly into just one of these criteria. The most
common missing measures are flexibility and innovativeness. All
categories and levels have at least one missing aspect. Only the
joint usage of all the measurement categories can provide a pos-
sibility of properly monitoring logistics performance (Shepherd
and Günter 2006).
Although extensive research has been conducted to find the
factors impacting the supply chain and transportation industry,
there is a significant knowledge gap in pinpointing which of these
factors impact the freight forwarding industry, specifically for air
and sea shipping. The current research contributes to the litera-
ture by bridging the identified knowledge gaps in the SOPs, strat-
egies and policies developed over time in the studied global firm’s
operations compared to today’s industry requirements. A critical
examination of the literature suggests the following knowledge
gaps which are addressed in this article:
• assessment of the operational performance of a freight
forwarding firm by developing an internal benchmarking
tool considering both tangible and intangible measures is
missing;
• a holistic approach encompassing strategies and systems
orientation in the development of an internal benchmark-
ing tool is also missing;
• an approach to systematically collating measures for evalu-
ating the performance of freight forwarding firms using the
prevailing factors is unavailable;
• identification, assessment and transfer of the best opera-
tional practices of a logistics company derived from inter-
nal benchmarking has not been reported; and
• scope for improving future operational strategies to ame-
liorate operational performance in the areas of internal
coordination, use of technology, resource allocation, exter-
nal coordination/communication and software upgrada-
tion has not been reported.
The global logistics firm is a part of world-leading transporta-
tion and logistics corporation Deutsche Bahn AG. The firm offers
integrated freight forwarding services from a single source. The
firm’s seamless transportation chains across all carriers – includ-
ing freight train, truck, ship and airplane – are combined with
complex additional logistical services. It has a strong global
presence in 140 countries.
The firm’s reputation is premised upon performance and
service, irrespective of the complexity of the logistics tasks and
requirements. As it constantly seeks to act with increasing speed
and flexibility on a global scale there is a need for continuous
improvement. Locally, it operates in Dubai, UAE, and provides a
complete range of international air and ocean freight forward-
ing services, together with integrated logistics services from its
premises in Dubai and Abu Dhabi.
The firm currently employs a multi-domestic strategy for its
operations, which has worked relatively well in the past. This
strategy enables the firm to customise its products to meet
the needs of each local market. The multi-domestic approach
also ensures that the firm can quickly and quite effectively
PRODUCTION PLANNING & CONTROL 617
3.1. The ocean freight division
In the UAE region, the firm’s ocean freight operations division
comprises import and export sub-departments, each manned
by a team of 34 employees. The teams are further divided into
sub-teams working on full container loads (FCL), less than full
container loads (LCL), and the hub team (HUB), the latter being
responsible for consolidating the LCLs into a single container.
These departments collaborate to provide the following core
operations (Table 2).
3.2. The air freight division
The firm’s air freight in the UAE offers a variety of operations,
as depicted in Table 2. Broadly, air freight is classified into two
departments, viz. air exports and air imports and all of these
operations are provided by these two departments.
3.3. Service scheduling approach
The usage of scheduling approaches, Make to Order (MTO)
or Design to Order (DTO), necessitates a massive emphasis
adapt to any changes in the marketplace. Hence, it has helped
the firm to develop a variety of product offerings. The organ-
isation’s UAE division is further divided into air freight, ocean
freight, sea-air freight, exhibition, contract logistics and oil
and gas.
This research aims to develop a deeper understanding of the
firm’s own internal processes, through which the current gaps in
the firm’s operations may be identified and sources of continuous
improvement suggested. The internal benchmarking tool in this
study measures and compares the performance of the following
four of the firm’s UAE operating departments:
• Ocean export;
• Ocean import;
• Air export; and
• Air import.
The operations of the firm’s four freight forwarding departments
are discussed in brief in the following sections to develop under-
standing of the firm’s current operations. An overview of the
export and import operations of the freight forwarding firm is
illustrated in Figure 1.
Figure 1. overview of the export and import operations of the freight forwarding firm.
notes: dn – delivery note, lPo – local purchase order, co – country of origin, BoE – bill of entry.
618 A. BHATTACHARYA AND D. A. DAVID
questionnaire was developed to analyse the results of the first
questionnaire, as it is necessary to understand the relative
weightage to be applied to the factors previously identified.
4.2. Data collection procedure
The data were collected from the respondents over two sepa-
rate intervals. The first questionnaire was administered at the
beginning of the research, while the second questionnaire was
administered towards the end of the study, approximately six
months after the first questionnaire was administered. Printed
survey forms were used, together with online data collection
procedures, such as Google Forms. The latter were used as most
of the external stakeholders could not be contacted offline.
Additional data were collected using observations and inter-
views over the span of six months. The respondents include sev-
eral members from the firm’s operations departments, including
management.
4.3. Sample criteria and design
A total sample size of 155 respondents was selected, includ-
ing members from the operations, finance, marketing and HR
departments, and external stakeholders, including employees
from several shipping and airline companies, local truckers and
haulers. Respondents from numerous companies participated
in the survey, including DNATA, Emirates Air, shipping liners
(such as Maersk) and other freight forwarders (such as Kuehne
Nagel). The employees of these companies were selected based
on the following criteria:
• working in the logistics department of any firm based in
the UAE;
• possessing a sound knowledge of the functioning of the
freight forwarding industry in the UAE; and
• having practical experience in logistics, specifically
transportation.
As required by the second research objective, it is necessary
to ensure that the respondents (i.e. internal stakeholders) are
employees of the UAE division of the studied global logistics
firm. These respondents include members from the core man-
agement, employees from operations and representatives from
all supporting departments.
on strong internal and external communication (Wang and
Rosenshine 1983). Conversely, a multi-domestic strategy places
less emphasis on extensive communication in terms of infor-
mation sharing between counterparts as compared to a trans-
national strategy (Segal-Horn and Faulkner 1999). Later in this
research, it is explored whether this strategy has a major impact
on the factors influencing performance in the freight forwarding
industry.
To develop an internal benchmarking tool for measuring the
performance of the studied departments, the factors driving
the target global logistics firm are identified. Prioritisation of the
firm’s operations narrows down these factors to those most rel-
evant factors. A quantitative approach supported by statistical
techniques is employed to facilitate systematic empirical investi-
gation. This study examines the quantified data, condensing the
results collected from the target population sample to measure
the incidence of various views and opinions within the chosen
sample. Further, analysis of the data obtained from the firm is
performed based on the identified parameters.
A multiple method approach (Figure 2) is adopted in this
empirical action research, including a series of interviews, ques-
tionnaire-based surveys and data collected by observation of
the processes. These yield real-world data to measure the per-
formance of the firm’s various departments, which influence the
formation of the internal benchmarking tool and ultimately serve
to measure internal performance.
To meet the first objective, only qualitative data are used, by
administering the questionnaire. A mixture of both quantitative
and qualitative data is used to address the second objective. A
factor analysis is performed on the qualitative data to investigate
the variable relationships. Every identified factor is measured and
included in the benchmarking tool, irrespective of its financial and
non-financial dimensions. The inclusion of these factors is further
justified by the addition of varied weightage given to each factor
as per the firm’s vision, management and employees.
4.1. Instrument development
A questionnaire was developed to identify the current trends
in the freight forwarding industry by identifying the priori-
ties provided to the factors affecting their business. A second
Table 2. operations of the firm’s freight division.
Operations Description of the ocean freight division’s operations
ocean freight FirMcomplete a solution for full-container requirements (Fcl transport)
FirMcombine consolidation of container part loads (lcl transport)
FirMskybridge combines the advantages of air and sea freight: twice as fast as sea transport; half the price of air transport
FirMicm integrated cargo Management: shipment organisation and control from purchase order through to delivery
FirMbeverages a comprehensive logistics solution for transporting wines and other spirits
FirMrecyclables a special solution for transporting recyclable paper, plastic, metal, and timber
FirMperishables a special sea freight solution for perishable consumer goods
air freight FirMjetcargo a fast and flawless service for airport-to-airport transport. there are three standard service packages for fixed periods, in addi-
tion to charter options to suit individual requirements
FirMjetxpress a premium product for door-to-door transport. there are no size or weight restrictions, and the service includes customs
clearance
FirMskybridge combines the advantages of air and sea freight: twice as fast as sea transport; half the price of air transport
FirMicm integrated cargo management: organisation and monitoring of shipments from order entry to delivery
FirMflightops this links the central hubs of every continent several times each week using the firm’s own services
PRODUCTION PLANNING & CONTROL 619
sections. In section one, the respondents are asked to provide
their views on the extent to which each of the identified factors
(i.e. indicators of firm’s performance) impacts freight forwarding
business today. This was to identify which of the factors are cur-
rently the most important in the freight forwarding business. A
factor analysis on these factors was then performed to identify
the most relevant factors.
The 10 factors identified through the literature were validated
and consolidated by interviewing several of the firm’s operations
experts, possessing years of experience in the freight forwarding
industry. The following factors were identified:
• Cost (De Toni and Tonchia 2001; Gunasekaran 2001);
• Quality of service (Tan 2001);
4.4. Profile of the respondents
Ten attributes for the freight forwarding industry were identified
from secondary data available in the literature. The survey ques-
tionnaires containing these factors were distributed among 155
freight forwarding and logistics professionals who have worked
in the UAE. People from top management, operations, finance/
HR and marketing/customer services departments were the
respondents who participated in this research.
4.5. Scale development and data analysis
A seven-point Likert scale was used, as an interval scale is neces-
sary for factor analysis. The questionnaire was divided into two
Figure 2. the research methodology.
620 A. BHATTACHARYA AND D. A. DAVID
impacting the decisions of freight forwarders in the UAE today.
The data were analysed using SPSS v.22.
4.6. Application of relevant weights
As this research aims to develop deliverables for a specific organ-
isation, it is important that every result should be aligned with
the target firm’s vision, mission and objectives (Desmidt 2016).
The firm’s current approach does not provide the weights that
should be assigned to the identified factors. Thus, a weighted
average approach was applied to the results of the second ques-
tionnaire, which was administered to the same set of respond-
ents. The intention was to analyse and identify which of the
above factors should be given greater priority as compared to
the others illustrated in the process mapping diagram (Figure 3).
• Quality of data (Schönsleben 2004);
• Resource utilisation (Chan and Qi 2003);
• Efficiency of SOP (Neely and Gregory 1995);
• Flexibility (Beamon 1999);
• Transparency (Chan and Qi 2003);
• Innovativeness (Chan and Qi 2003);
• Consistency of service (Tan 2001); and
• On-time delivery (Schönsleben 2004).
These factors are the inputs to the factor analysis, for which
they were re-named: cst, servqual, servdata, util, eff_of_sop,
flex, transparency, innovation, constncy, and on_time
respectively.
The collected data were analysed using descriptive statistics,
reliability analysis, and factor analysis to identify the key factors
Figure 3. Process mapping with cycle time and steps for the ocean freight exports department.
PRODUCTION PLANNING & CONTROL 621
4.10. Measurement of service quality
Perceived service quality includes the quality of data, quality of
service and the consistency or reliability of the service offered.
The firm uses a tool, known as ‘Events’, which measures the data
quality, data consistency and data reliability. Quality scores for
the benchmarking tool have to incorporate additional data, such
as each department’s inclination towards assigning additional
processes to maintain reliability in the sent data. The integration
of these data along with the data received from the ‘Events’ tool
assists the assessment of each department’s inclination towards
quality maintenance during the study period.
Once the methodology was finalised and established, data
were collated from the firm and the obtained results were
analysed. This was undertaken to assess the operational per-
formance of the departments, based on the relevant factors
impacting the freight forwarding industry, which would assist
in developing the benchmarking tool and suggest future oper-
ational strategies.
5.1. Reliability analysis
Factor analysis is a widely utilised statistical technique (Beavers
et al. 2013). The technique continually refines and compares
solutions through a cyclical process until the most meaningful
solution is reached (Tabachnick and Fidell 2001). Factor analy-
sis was used in this research to reduce the number of variables,
establish underlying relationships between the measured varia-
bles and constructs, and provide construct reliability and valid-
ity. This was done using the Kaiser–Meyer–Olkin (KMO) test and
Bartlett’s test (Table 3). These tests measure the strength of rela-
tionships among the variables. In the KMO test, an α value of 0.5
and above indicates a good reliability for the scale (Cerny and
Kaiser 1977; Kaiser 1974). The KMO test result, α = 0.849, indicates
that the scale has good reliability. This confirms that the sample
is adequate for the study. The Bartlett’s test confirmed that the
test of sphericity is significant (0.000), i.e. the significance level is
small enough to reject the null hypothesis. This means that the
correlation matrix (Table 4) is not an identity matrix.
It is observed that the cost, service quality, service data, utility,
efficiency of SOP, flexibility, transparency, innovation, consistency
and on-time delivery variables are highly correlated amongst
themselves. The correlations across cost and service quality, cost
and service data, cost and efficiency of SOP, cost and transpar-
ency, cost and innovation, cost and consistency and cost and
on-time delivery is comparatively small.
4.7. Application of relevant sub-weights
The pool of respondents – comprising employees, management
and external stakeholders – were asked to report their priorities.
Each of them responded with respect to their individual priori-
ties. An addendum to the second question was thus added only
for the firm’s UAE top management, who were asked the follow-
ing question: ‘Which of the above respondents are to be given
higher priority?’. This process aimed to assign priorities to each
respondent and thus prioritise consistently with the top man-
agements’ perspectives and, hence, fulfil the firm’s vision.
4.8. Measurement of operational costs
To develop the benchmarking tool, live data from the produc-
tion environment was taken with regard to the above factors
and integrated with the designated weights to assess the actual
performance of the studied departments. It is relatively easier
to measure the rolling cost of operations for each of the stud-
ied departments as each quarter’s financial summary is metic-
ulously maintained by the finance department. These costs
include all the variable costs for quarter 3 of 2015, which range
from staff salaries to machine maintenance, even down to cap-
turing the money spent on stationery.
4.9. Measurement of processes’ efficiency
Most organisations today are compelled to measure their finan-
cial performance every quarter; some even move beyond this by
building tools to measure conformity with service-level agree-
ments (SLAs) and efficiency. However, very few organisations
measure the efficiency of their defined SOPs. Understandably,
the measurement of SOPs is an arduous and time-consuming
undertaking. The measurement of the efficiency of SOPs, ser-
vice consistency and time of delivery are crucial for internal
benchmarking.
Therefore, each of the department’s operations was measured
and timed. This necessitated measurement of the cycle time in
terms of the time taken to process one standard package or con-
tainer. This is reflected in the process mapping diagrams for the
ocean (Figure 3) and air freight exports and imports departments
(Figures A1–A3).
Table 3. KMo test and Bartlett’s test for sample adequacy.
Kaiser–Meyer–olkin measure of sampling adequacy 0.849
Bartlett’s test of sphericity approx. χ2 1137.903
df 45
Sig. 0.000
Table 4. correlation matrix table.
cst servqual servdata util eff_of_sop flex transparency innovation constncy on_time
cst 1.000 0.268 0.288 0.502 0.211 0.341 0.285 0.282 0.296 0.262
servqual 0.268 1.000 0.791 0.252 0.278 0.446 0.377 0.507 0.764 0.354
servdata 0.288 0.791 1.000 0.401 0.364 0.468 0.465 0.502 0.750 0.417
util 0.502 0.252 0.401 1.000 0.448 0.465 0.394 0.378 0.279 0.456
eff_of_sop 0.211 0.278 0.364 0.448 1.000 0.753 0.755 0.613 0.206 0.818
flex 0.341 0.446 0.468 0.465 0.753 1.000 0.813 0.561 0.399 0.831
transparency 0.285 0.377 0.465 0.394 0.755 0.813 1.000 0.616 0.341 0.836
innovation 0.282 0.507 0.502 0.378 0.613 0.561 0.616 1.000 0.416 0.510
constncy 0.296 0.764 0.750 0.279 0.206 0.399 0.341 0.416 1.000 0.306
on_time 0.262 0.354 0.417 0.456 0.818 0.831 0.836 0.510 0.306 1.000
622 A. BHATTACHARYA AND D. A. DAVID
findings of Mundfrom, Shaw, and Ke (2005). As observed from
Table 6, three factors (i.e. components) can be extracted from
the data where all the factor loadings that permit assignment of
an item to a specific factor exceed 0.291.
The first factor includes two items, viz. efficiency of SOP and
on-time delivery, and explains 37.51% of the variance. This fac-
tor could be termed ‘efficiency of processes’. The second factor,
termed ‘perceived quality’, encompasses quality of service, quality
of data and consistency, and explains 27.2% of the variance. The
third factor, termed ‘cost effectiveness’, includes cost and resource
utilisation, and explains 15.57% of the variance. These three fac-
tors together explain 80.30% of the variance (Table 7). It can be
seen that, starting from factor 4 onwards, the factors have an
eigenvalue of less than 1; therefore, only first three factors were
retained for further analysis. Through the aforementioned anal-
ysis the three factors broadly realised comprise:
• efficiency of processes;
• perceived quality; and
• cost effectiveness.
5.3. Application of weights
This section provides insight into the parameters on which
the performance of each of the defined departments could be
measured. Though the parameters are rudimentary, they define
the core premise of the workings of the logistics industry today.
Each of the parameters identified are conflicting in nature.
Therefore, assignment of equal weightage to all of these param-
eters would be an incorrect approach. In developing the internal
benchmarking tool for the firm, its vision, objectives and mis-
sion must be considered by attributing appropriate weight to
each factor.
The administration of the second questionnaire revealed the
propensities of each department towards each of the factors
and sub-factors (Table 8). Figures reveal the firm’s upper man-
agement’s inclination towards the priority to be given to each of
the respondents, and the propensity of external stakeholders and
the firm’s operations department, marketing/customer services/
sales department, HR/finance department and top management,
respectively, towards the factors. With the factors and weights
thus identified, it is possible to measure the actual parameters
considering the management’s priorities. The results are detailed
in Tables 8 and 9.
The table of communalities (Table 5) indicates how much of
the variance in the variables is accounted for by the extracted fac-
tors. The ‘Extraction’ value is the proportion of variance that each
variable has in common with other variables. For example, it is
revealed that 86.7% of the variance in ‘service quality’ is accounted
for, while 57.7% of the variance in ‘innovation’ is accounted for. A
communality value of more than 0.5 (Beavers et al. 2013; Costello
and Osborne 2005) is considered necessary for further analysis.
Therefore, all of the variables can be analysed further.
5.2. Exploratory factor analysis
Exploratory factor analysis is used to determine the correlation
among different variables. This analysis focuses on grouping the
variables based on strong correlations (Levine 2015). In total, a
useable sample size of 155 questionnaires each containing 10
factors suggests that the study has exceeded the minimum
requirement for case-to-item ratio. This is consistent with the
Table 5. communalities.
note: Extraction method: Principal component analysis.
Initial Extraction
cst 1.000 0.813
servqual 1.000 0.867
servdata 1.000 0.833
util 1.000 0.726
eff_of_sop 1.000 0.848
flex 1.000 0.825
transparency 1.000 0.843
innovation 1.000 0.577
constncy 1.000 0.833
on_time 1.000 0.864
Table 6. component scores and coefficient matrix.
notes: Extraction method: Principal component analysis. rotation method:
Varimax with Kaiser normalisation.
Table 7. total variance table.
PRODUCTION PLANNING & CONTROL 623
the importance of this factor, it has already implemented soft-
ware for measuring the quality of data, data availability and con-
sistency. Aside from company-specific sensitive information, the
top-level management allowed extraction of the events scores
for each department. The scores for the studied period are illus-
trated in Table 11.
5.6. Actual performance measurement of cost
effectiveness
The third factor comprised cost and resource utilisation.
Understandably, the firm’s top-level management was reluc-
tant to share confidential financial information. Therefore,
for the sake of comparison, ratios of the cumulative oper-
ating costs were identified ( Table 12). These costs include
everything from staff salaries to vehicle maintenance, and are
segregated departmentally. The costs span the entire studied
period.
5.4. Actual performance measurement of efficiency of
processes
The first factor encompasses efficiency of SOP and on-time deliv-
ery. To build the internal benchmarking tool, the individual per-
formance of each department was measured for these factors.
Thus, the SOPs of each department were thoroughly studied for
a period of seven months, using the same led to identifying the
cycle time of each department. The cycle time is the time taken
by each department to process and ship one standard package.
The cycle time (Table 10) of each department is found from each
department’s process maps.
5.5. Actual performance measurement of perceived
quality
The second factor encompasses quality of service, quality of
data and consistency, which together comprise customer qual-
ity perception. As the studied firm under had earlier identified
Table 8. Factor priority matrix table.
Top management
priority
(%)
Efficiency of processes Perceived quality Cost effectiveness
Efficiency of
SOP (%)
Maintenance of
low cycle times
(%)
Maintenance of
good quality of
data (%)
Maintenance in
consistency in
service delivery
(%)
Maintenance
of lower cost of
operations (%)
Efficient use of
manpower (%)
25 External stake-
holders
16 10 23 19 19 13
25 operations
department
17 11 24 16 20 12
9 Hr/Finance
department
14 12 25 17 20 12
29 top Management 15 9 21 20 23 12
12 Marketing/ cus-
tomer services
14 12 24 16 20 14
100 total weightage
Table 9. application of weights to factors table.
Table 10. departmental cycle time.
Department
Time spent on
inbound communi-
cation
Time spent on
recording and
sorting
Time spent on
outbound commu-
nication
Time spent on new
document creation
Total cycle time
(min/file)
Percentage contri-
bution
ocean Export 35 min/file 64.2 min/file 43 min/file 33 min/file 175.2 0.2916
ocean import 23 min/file 52 min/file 40 min/file 24 min/file 139 0.2314
air Export 22 min/file 55.5 min/file 42 min/file 33 min/file 152.5 0.2539
air import 23 min/file 51.5 min/file 36 min/file 24 min/file 134.5 0.2239
624 A. BHATTACHARYA AND D. A. DAVID
The operational performance measurement results are illus-
trated in Table 14. From Table 14, it is interpreted that ocean
exports is the firm’s best-performing department; it there-
fore becomes the benchmark for all of the other departments.
Overall, it is concluded that the firm’s exports sub-division is per-
forming relatively well compared to the imports sub-division.
On further analysis of the observed data, it was concluded that
the exports departments have significantly higher scores due to
the following reasons:
• consistent maintenance of high data quality scores, and
• operating under significantly lower costs compared to the
imports departments.
As quality and efficiency in utilising finances have been given
higher weightage in the internal benchmarking tool, viz. 40.97%
and 33.11%, respectively, the results are skewed towards them.
Figure 4(a) indicates that the imports departments’ SOP efficiency
is significantly better than that of the exports departments. The
exact figures in terms of dollar values could not be provided
in this article to protect the firm’s confidentiality. However, the
weighted the average ratios of each department’s costs provide a
representative comparison of the spending of each of the studied
departments. Figure 4(b) explores a non-weighted score compar-
ison of the factors for each department, which does not consider
the benchmarking tool. A close comparison of Figure 4(a) and (b)
reveals that inclusion of the strategic priorities of the firm’s vision,
objectives, and mission results in targeting different operational
performance measures in Figure 4(a), intended to benefit the
firm’s strategic goals.
6.1. Practical implications
The implementation of the internal benchmarking tool to
enhance the operational performance of this worldwide freight
forwarding giant explores a number of practical implications
in regard to operational strategies. These contribute to the five
main pillars of the studied global logistics firm: internal coordi-
nation, use of technology in the departments, resource alloca-
tion, external coordination and communication and software
upgradation.
(a) Internal co-ordination:
The results reveal that the air exports department scores
excellent points as their SOPs are very efficient. They have excel-
lent external collaboration with their suppliers, with better tools
for data processing, such as the M2 text generator used by the
air freight customs division. This tool significantly reduces the
amount of time spent on data entry tasks. However, this knowhow
is not shared across the organisation. If this tool were introduced
in the ocean freight division, it would significantly improve that
division’s SOPs. The failure to share process improvements across
5.7. The internal benchmarking tool
All the above findings were carefully selected and analysed to
realise the third objective: formulating the actual performance
measurement of each of the departments. The observed data
collected through all of the above techniques is consolidated
below.
The first observation is that the cycle time is the inverse of the
studied factor, i.e. efficiency of processes (Table 13). Thus, the
higher the cycle time value, the less efficient is the department’s
SOP. Similarly, cost and cost effectiveness are opposites, in the
sense that if the conserved costs for the department are high,
then it is not efficient in using its resources appropriately. Hence,
the said factors have been inverted and the normalised values
are found in Table 13.
The actual internal benchmarking performance of the firm’s
operations is not only based on observed values but also on the
parameters set by the freight forwarders. Relevant weights were
assigned with respect to the top-level management’s priority
over the rest of the stakeholders. The weights are highlighted in
blue in Table 9. These weights are integrated with the normalised
performance measures obtained from Table 13, resulting in the
operational performance measurement through internal bench-
marking (Table 14).
Table 11. Quality (events scores).
Depart-
ment
June 2015
(%)
July 2015
(%)
August
2015 (%)
Cumula-
tive Score
(%)
Per-
centage
Contribu-
tion
ocean
Export
99.93 99.73 99.19 99.94 0.2529
ocean
import
99.94 98.47 99.12 99.17 0.2509
air Export 99.13 99.35 96.43 98.30 0.2488
air import 97.32 97.43 98.43 97.72 0.2473
Table 12. the firm’s observed cost scores.
Department Ratio of cumulative operating costs
ocean Export 0.1137
ocean import 0.3045
air Export 0.1706
air import 0.4112
Table 13. observed scores for the logistics firm’s operations.
Department Cycle time Perceived quality Cost
ocean Export 0.316681072 0.252929416 0.113714
ocean import 0.240276577 0.25098069 0.304539
air Export 0.266724287 0.248778883 0.170571
air import 0.176318064 0.247311012 0.411176
Table 14. operational performance through the internal benchmarking tool.
PRODUCTION PLANNING & CONTROL 625
demand is seasonal and the number of new shipments is higher
than the air freight, a pool-based resource allocation structure
gives far more efficient results, especially as it ensures that all the
employees have practical experience regarding every job. The
number of idle employees is significantly smaller in the ocean
freight compared to air freight division.
(d) External coordination/communication:
There are several variations in export versus import processes
due to differences in their customs and process requirements.
The major difference between ocean and air freight operations
lies in the viable urgency from customers in the processing of air
shipments. Hence, the customer’s requirements, the documents
and the material often arrive only a few hours before, or often just
in time for, departure. This fact has shaped the firm’s air freight
division to make its operations more agile compared to its ocean
operations. For example, the ocean import department employs a
pigeonhole for efficient sorting of the shipments, whereas in the
air import department, a dedicated employee sorts and assigns
the jobs instantaneously.
Several other approaches to shorten the throughput time
are employed in air freight as compared to ocean freight due
to the aforementioned need for rapid turnaround. High supplier
integration is another example. Through collaboration with most
of its carriers, the air freight departments can now book airline
tickets through their internal enterprise resource planning (ERP)
tool, whereas the ocean freight departments must book through
the shipping liner websites. This means that they do not have to
wait a day to print the booking confirmation, thereby requiring
less manpower from both the firm and the airliner. This software
integration also means that the firm’s air exports department can
print the original airway bill on their own printers, whereas the
ocean exports department must spend hours of manpower and
incur costs by sending a runner every day to the carrier office to
collect the original ocean master bill of lading (MBL).
(e) Software Upgradation:
While studying the internal SOPs of each department, the bot-
tleneck processes were identified as steps 11 and 12: in essence,
every department spent considerable time on cost booking and
invoicing. Although these processes are essential to daily oper-
ations, the software interfaces are not user-friendly. Therefore,
there is a scope of significant improvement of SOP efficiency with
even module-specific upgrades focusing on easing the entry of
data into the database.
the departments accounts for a huge opportunity loss. There are
several communication gaps within and between the studied
departments.
(b) Technology:
During the process mapping stage, it was noted that the
worldwide freight forwarding giant employs a multi-domestic
approach, especially in terms of information gathering and stor-
age. Each of their local offices has an individual set of databases.
Information between the firm’s regional offices is seldom shared.
For example, if there is a shipment between the firm’s regional
offices based in Dubai and Italy, it is triggered by the Italy office.
The possibility of the shipper/consignee information being stored
in the Italy database is quite high, as the shipment is triggered
from there. However, the sharing of this information is limited
since the local databases are not completely integrated. The
firm’s Dubai office would have to re-create the shipper/consignee
information by gathering and entering all the details about the
Italian shipper/consignee in their local database. This activity is
time-consuming, especially as the firm’s operations in Dubai do
not have the authority to create/update any user in the database.
(c) Resource allocation:
The firm’s multi-domestic strategy dictates that the current
resource allocation structure is substantially influenced by exter-
nal demand patterns, in terms of the number and types of con-
tracts won by the firm’s sales teams for that fiscal year. Demand
patterns for ocean freight generally consist of few shipments to
varied destinations, and the number of new customers (shipper
and consignees) are significantly higher than for air freight, for
which the bulk of the shipment orders come in the form of sev-
eral long-term contracts. Hence, the operational department’s
job allocation structure for each of these departments has been
formulated to ensure that each of the departments performs
highly on local responsiveness, in tandem with the multi-do-
mestic strategy.
The air freight departments have arranged for their employees
to service specific clients, resulting in client service with greater
efficiency, lower throughput times and flexibility. The service is
less formalised as compared with other departments as the num-
ber of steps needed to process these shipments is significantly
reduced in terms of complexity and time.
The complexity in handling an ocean shipment is far greater in
comparison with air freight due to the higher levels of standard-
isation in the current pool-based resource allocation system. As
(a) Score comparison obtained from the (b) Non-weighted score comparison
internal benchmarking tool
0
0.05
0.1
0.15
Ocean
Export
Ocean
Import
Air Export Air Import
Efficiency of processes Perceived quality. Cost effectiveness
0
0.1
0.2
0.3
0.4
Ocean
Export
Ocean
Import
Air Export Air Import
Efficiency of processes Perceived quality. Cost effectiveness
Figure 4. Score comparison with and without internal benchmarking tool.
626 A. BHATTACHARYA AND D. A. DAVID
assess, measure and improve operational performance of the
departments in a global logistics firm. Several knowledge gaps
are identified from a critical examination of the literature. The
four research questions enumerated in Section 1 have been
answered through the outcomes of this pragmatic research. The
outcomes of this research, through an in-depth action research
and a series of statistical tests, enable the global logistics firm
to form a deeper understanding of their own internal processes
and metrics and contribute to better operational performance.
The outcomes derived from the internal benchmarking tool
provide the ‘best practice’ which forms an intermediate step
towards external benchmarking. The implementation of the
internal benchmarking tool explores several operational and
strategic recommendations for the studied global logistics firm
to achieve better operational performance. Further, several
theoretical implications are derived to improve the operational
performance of the logistics firm. It is found that the firm’s mul-
ti-domestic and localised strategies have a major impact on the
factors influencing the performance of its freight forwarding
business. The research outcomes facilitate investigating the cur-
rent business strategies, the SOPs and the scope of improving
those.
The main purpose of developing the internal benchmarking
tool was not to discover the best-performing department among
the four studied but rather to find the reasons why it is perform-
ing better than the others and, simultaneously, to examine if its
process improvements could be disseminated across the firm’s
various other departments. The lessons of this study’s internal
benchmark are clear. The global logistics firm’s exports depart-
ments generally fared better than its imports departments due
to the following reasons. One of the main reasons for the lower
costs and higher perceived quality of the exports departments is
attributable to the employees. The number of employees in the
exports departments, especially ocean exports, is far lower than
the employee number in the imports departments, resulting in
lower personnel costs. The export departments’ employees have
been working in the firm for more than seven years and they are
solely responsible for the excellent quality scores and increased
inter-departmental communication. This implies that a smaller
team of more experienced employees is preferable to the import
departments’ strategy of engaging a high number of less expe-
rienced employees.
Although the imports departments were not identified as the
benchmark, they did achieve higher scores regarding efficiency
of processes in comparison with the exports departments. The
SOP for the import departments, especially air imports, has been
constantly updated by the firm over time to ensure fulfilment of
large incoming orders. Consequently, there have been dozens
of software upgradations to the existing systems, with the sole
purpose of integrating them with those of the major suppliers,
including Emirates, Etihad and other carriers. This implies that
further improvement of SOPs could be achieved though further
software integration with the major suppliers, as this would save
time in both co-ordination and external communication. Another
interesting observation is that only the air imports department
actually owns a fleet of trucks. This increases the efficiency of the
internal processes and greatly helps to reduce the time spent on
external communication, compared to the other departments
that continue to rely on external haulers to transport packages
6.2. Theoretical implications for logistics industry
Through implementation of the internal benchmarking tool
the following set of theoretical implications are observed to
improve operational performance in a logistics firm:
• An improved means of internal communication and kno-
whow should be consistently maintained, not only within
the logistics firm’s division but also across all of its offices
globally. There should be a strategic shift towards a trans-
national movement from the currently followed multi-do-
mestic strategy. Substantial emphasis should be focused
on internal and external collaboration to improve opera-
tional performance.
• If the firm employs a transnational strategy, the time spent
and data capacity required to store duplicate information
could be eliminated, as a single global database is able to
store all of the firm’s records.
• The demand patterns for ocean and air freight departments
are substantially seasonal. Therefore, it is recommended that
the firm should employ a mix of multi-domestic and local-
ised strategies for job allocation, leading to an improved
operational performance. As the numbers and sizes of ship-
ments vary often, emphasis should be placed upon contin-
uous improvement, as envisaged in Coulter, Baschung, and
Bititci (2000) and MacKerron, Masson, and McGlynn (2003),
in terms of the existing job allocation method employed.
• The acquired wisdom from air freight operations regard-
ing external coordination/communication can be imple-
mented within ocean freight. High supplier collaboration
and many other benefits would also facilitate shorter pro-
cessing times, thus increasing also the overall operational
efficiency and performance of the ocean departments.
• An upgrade of the software can facilitate lowering com-
munication barriers within and across the organisation,
thereby improving operational performance.
The ‘best practice’ derived from internal benchmarking is an
intermediate step towards external benchmarking. These best
practices can be transferred to other departments of the firm.
Therefore, the benchmarking tool enables departments to inte-
grate to some extent by sharing the operations processes of com-
mon strategies. The firm’s multi-domestic strategy, coupled with
its local strategies, strengthens its operations in terms of respon-
siveness. Thus, an appropriate performance measurement seeks
to thoroughly investigate the firm’s operations through process
mapping, which in turn facilitates assessing the performances of
disparate functional entities. Consideration of both the tangible
and intangible measures benefits the firm in assessing the current
operational situation. This is consistent with the study of Karia and
Wong (2013). The firm’s strategic priorities must be integrated
with its operational performance to ascertain the effective perfor-
mance of the firm. This is consistent with those reported in earlier
studies on benchmarking and performance (Coulter, Baschung,
and Bititci 2000; Meybodi 2009).
This article reports a paradigm shift by designing and imple-
menting a novel and holistic internal benchmarking tool to
PRODUCTION PLANNING & CONTROL 627
Adebanjo, D., A. Abbas, and R. Mann. 2010. “An Investigation of the Adoption
and Implementation of Benchmarking.” International Journal of Operations
& Production Management 30 (11): 1140–1169.
Ahmad, N., and R. Mehmood. 2016. “Enterprise Systems and Performance of
Future City Logistics.” Production Planning & Control 27 (6): 500–513.
Amaral, P., and R. Sousa. 2009. “Barriers to Internal Benchmarking Initiatives:
An Empirical Investigation.” Benchmarking: An International Journal 16 (4):
523–542.
Andersen, B., and P. Jordan. 1998. “Setting up a Performance Benchmarking
Network.” Production Planning & Control 9 (1): 13–19.
Anderson, K., and R. McAdam. 2004. “A Critique of Benchmarking and
Performance Measurement.” Benchmarking: An International Journal 11
(5): 465–483.
Beamon, B. M. 1999. “Measuring Supply Chain Performance.” International
Journal of Operations & Production Management 19 (3): 275–292.
Beavers, A. S., J. W. Lounsbury, J. K. Richards, S. W. Huck, G. J. Skolits, and S.
L. Esquivel. 2013. “Practical Considerations for Using Exploratory Factor
Analysis in Educational Research.” Practical Assessment, Research &
Evaluation 18 (6): 1–13.
Bevilacqua, M., F. E. Ciarapica, and I. D. Sanctis. 2017. “Lean Practices
Implementation and Their Relationships with Operational Responsiveness
and Company Performance: An Italian Study.” International Journal of
Production Research 55 (3): 769–794.
Binder, M., B. Clegg, and W. Egel-Hess. 2006. “Achieving Internal Process
Benchmarking: Guidance from BASF.” Benchmarking: An International
Journal 13 (6): 662–687.
Camp, R. C. 1995. Business Process Benchmarking: Finding and Implementing
Best Practices. Milwaukee, WI: ASQC Quality Press.
Caplice, C., and Y. Sheffi. 1995. “A Review and Evaluation of Logistics
Performance Measurement Systems.” The International Journal of Logistics
Management 6 (1): 61–74.
Cassell, C., S. Nadin, and M. O. Gray. 2001. “The Use and Effectiveness of
Benchmarking in SMEs.” Benchmarking: An International Journal 8 (3):
212–222.
Cerny, Barbara A., and H. F. Kaiser. 1977. “A Study of a Measure of Sampling
Adequacy for Factor-Analytic Correlation Matrices.” Multivariate Behavioral
Research 12 (1): 43–47.
Chan, F. T. S., and H. J. Qi. 2003. “An Innovative Performance Measurement
Method for Supply Chain Management.” Supply Chain Management: An
International Journal 8 (3): 209–223.
Chung, T. W., W. C. Ahn, S. M. Jeon, and V. Van Thai. 2015. “A Benchmarking
of Operational Efficiency in Asia Pacific International Cargo Airports.” The
Asian Journal of Shipping and Logistics 31 (1): 85–108.
Colicchia, C., A. Creazza, and F. Dallari. 2017. “Lean and Green Supply Chain
Management through Intermodal Transport: Insights from the Fast
Moving Consumer Goods Industry.” Production Planning & Control 28 (4):
321–334.
Costello, A. B., and J. W. Osborne. 2005. “Best Practices in Exploratory Factor
Analysis: Four Recommendations for Getting the Most from Your Analysis.”
Practical Assessment, Research & Evaluation 10 (7): 1–9.
Coulter, J., N. S. Baschung, and U. S. Bititci. 2000. “Benchmarking for Small- to
Medium-sized Enterprises.” Production Planning & Control 11 (4): 400–408.
Dattakumar, R., and R. Jagadeesh. 2003. “A Review of Literature on
Benchmarking.” Benchmarking: An International Journal 10 (3): 176–209.
De Toni, A., and S. Tonchia. 2001. “Performance Measurement Systems –
Models, Characteristics and Measures.” International Journal of Operations
& Production Management 21 (1/2): 46–71.
Desmidt, S. 2016. “The Relevance of Mission Statements: Analysing the
Antecedents of Perceived Message Quality and Its Relationship to
Employee Mission Engagement.” Public Management Review 18 (6): 894–
917.
Francis, J. 2008. “Benchmarking: Get the Gain.” Supply Chain Management
Review 12 (4), April: 22–29.
Geanuracos, J. 1994. The Global Performance Game. New York: Crossborder.
Gilmour, P. 1999. “Benchmarking Supply Chain Operations.” International
Journal of Physical Distribution & Logistics Management 29 (4): 283–290.
from and to the ports. However, faster haulage and SOP effi-
ciency have high intrinsic costs, as the cost of operations for
the air imports department increases by the addition of vehicle
maintenance and drivers’ payroll expenses. The firm has to decide
whether this trade-off justifies the required costs.
7.1. Scope for future research
The internal benchmarking tool can facilitate careful examina-
tion to identify any scope to reduce waste (Tseng et al. 2014)
from operational processes. This will lead to achieving lean
operations. Adequate thrust can be provided to innovation
capabilities of the firm (Yang 2012), which is currently a weak-
ness within such firms. Through logistics process innovation,
lean approaches (Bevilacqua, Ciarapica, and Sanctis 2017;
Colicchia, Creazza, and Dallari 2017; Godinho Filho, Ganga, and
Gunasekaran 2016; Negrão, Filho, and Marodin 2017; Panwar et al.
2015; Panwar et al. 2018) can be devised and implemented. This
area provides significant scope for further research through the
internal benchmarking tool.
The authors sincerely convey thanks to the three anonymous reviewers for
their constructive comments. The authors also acknowledge the assistance
provided by the executives of the anonymous German global logistic firm
based in Dubai, UAE, who had provided support to carry out the work.
No potential conflict of interest was reported by the authors.
Arijit Bhattacharya is working as a senior lecturer in
Operations and Supply Chain Management at Norwich
Business School of the University of East Anglia. He
worked at the University of Dubai, Brunel University
London, Dublin City University and Patent Office, India.
His research interests are in sustainable operations and
supply chain management. Till date, he has published
more than 70 articles in prominent international journals,
refereed conferences and book chapters. He guest edited
special issues of prominent journals. Bhattacharya is a reviewer of many
prominent international journals. He is associated with refereed interna-
tional conferences as a programme committee member.
Dhyan Albert David is currently working as a materials
manager at Hilti META, Dubai. He is responsible for power
tool accessories, tool inserts and diamond tools business
units. He is also responsible for measuring equipment for
marketing organisations in Qatar and Bahrain. He did his
MBA majoring operations and logistics management
from Dubai Business School of the University of Dubai
and T.A. Pai Management Institute, India. His undergrad-
uate degree was in electronics and communication engi-
neering from St Joseph Engineering College, Mangalore, India.
ORCID
Arijit Bhattacharya http://orcid.org/0000-0001-5698-297X
http://orcid.org
http://orcid.org/0000-0001-5698-297X
628 A. BHATTACHARYA AND D. A. DAVID
Panwar, A., R. Jain, A. P. S. Rathore, B. Nepal, and A. C. Lyons. 2018. “The
Impact of Lean Practices on Operational Performance – An Empirical
Investigation of Indian Process Industries.” Production Planning & Control
29 (2): 158–169.
Salem, M. S. M. 2010. “An Application of the Analytic Hierarchy Process to
Determine Benchmarking Criteria for Manufacturing Organisations.”
International Journal of Trade, Economics and Finance 1 (1): 93–102.
Schönsleben, P. 2004. Integral Logistics Management: Planning & Control of
Comprehensive Supply Chains. Boca Raton, FL: CRC Press.
Segal-Horn, S., and D. Faulkner. 1999. The Dynamics of International Strategy.
London: International Thomson Business Press.
Shepherd, C., and H. Günter. 2006. “Measuring Supply Chain Performance:
Current Research and Future Directions.” International Journal of
Productivity and Performance Management 55 (3/4): 242–258.
Simatupang, T., and R. Sridharan. 2004. “Benchmarking Supply Chain
Collaboration.” Benchmarking: An International Journal 11 (1): 9–30.
Soni, G., and R. Kodali. 2010. “Internal Benchmarking for Assessment of
Supply Chain Performance.” Benchmarking: An International Journal 17
(1): 44–76.
Southard, P. B., and D. H. Parente. 2007. “A Model for Internal Benchmarking:
When and How?” Benchmarking: An International Journal 14 (2): 161–171.
Spendolini, M. 1994. The Benchmarking Book. New York, NY: Amacom Books.
ISBN: 978-0814450772.
Suzuki, S. 2015. “SCM Logistics Scorecard: A Simplified Benchmarking
Tool for Supply Chain Operational Performance.” In Proceedings of the
IEEE International Conference on Industrial Engineering and Engineering
Management, 290–294.
Tabachnick, B., and L. Fidell. 2001. Using Multivariate Statistics. Needham
Heights, MA: Allyn & Bacon.
Tan, K. 2001. “A Framework of Supply Chain Management Literature.”
European Journal of Purchasing & Supply Management 7 (1): 39–48.
Tseng, M.-L., K.-H. Tan, M. Lim, R.-J. Lin, and Y. Geng. 2014. “Benchmarking
Eco-Efficiency in Green Supply Chain Practices in Uncertainty.” Production
Planning & Control 25 (13–14): 1079–1090.
Tutcher, G. 1994. “How Successful Companies Improve through Internal
Benchmarking.” Managing Service Quality: An International Journal 4 (2):
44–46.
Van Landeghem, R., and K. Persoons. 2001. “Benchmarking of Logistical
Operations Based on Causal Model.” International Journal of Operations &
Production Management 21 (1/2): 254–267.
Voss, C. A., P. Åhlström, and K. Blackmon. 1997. “Benchmarking and
Operational Performance: Some Empirical Results.” International Journal
of Operations & Production Management 17 (10): 1046–1058.
Wang, M., and M. Rosenshine. 1983. “Scheduling for a Combination of Made-
to-Stock and Made-to-Order Jobs in a Job Shop.” International Journal of
Production Research 21 (5): 607–616.
Wie, W. 2014. “Performance measurement of manufacturing supply chain.”
Thesis submitted for the Degree of Master of Applied Science of Quality
Systems Engineering at Concordia University, Montreal, Quebec, Canada,
10–69.
Wong, W. P., and K. Y. Wong. 2008. “A Review on Benchmarking of Supply
Chain Performance Measures.” Benchmarking: An International Journal 15
(1): 25–51.
Yang, C.-C. 2012. “Assessing the Moderating Effect of Innovation Capability
on the Relationship between Logistics Service Capability and Firm
Performance for Ocean Freight Forwarders.” International Journal of
Logistics Research and Applications 15 (1): 53–69.
Yasin, M. M. 2002. “The Theory and Practice of Benchmarking: Then and
Now.” Benchmarking: An International Journal 9 (3): 217–243.
Zairi, M. 1996. Benchmarking for Best Practices. Oxford: Butterworth-
Heinemann.
Godinho Filho, M. G., G. M. D. Ganga, and A. Gunasekaran. 2016.
“Lean Manufacturing in Brazilian Small and Medium Enterprises:
Implementation and Effect on Performance.” International Journal of
Production Research 54 (24): 7523–7545.
Gunasekaran, A. 2001. “Benchmarking in Supply Chain Management.”
Benchmarking: An International Journal 8 (4): 1.
Gunasekaran, A. 2002. “Benchmarking in Logistics.” Benchmarking: An
International Journal 9 (4): 1.
Gunasekaran, A., C. Patel, and E. Tirtiroglu. 2001. “Performance Measurement
and Metrics in a Supply Chain Environment.” International Journal of
Operations & Production Management 21 (1/2): 71–87.
Hanman, S. 1997. “Benchmarking Your Firm’s Performance with Best Practice.”
The International Journal of Logistics Management 8 (2): 1–18.
van Hoek, R. 2000. Logistics and the Extended Enterprise: Benchmarks
and Best Practices for the Manufacturing Professional. Supply Chain
Management: An International Journal 5 (2): 110–110.
Hyland, P., and R. Beckett. 2002. “Learning to Compete: The Value of Internal
Benchmarking.” Benchmarking: An International Journal 9 (3): 293–304.
Julien, F. W. 1993. “The Power of Benchmarking.” The Internal Auditor 50 (4):
22.
Kablan, M., and F. Dweiri. 2003. “A Mathematical Model for Maximizing the
Overall Benchmarking Effectiveness without Exceeding the Available
Amounts of Resources.” Production Planning & Control 14 (1): 76–81.
Kaiser, H. 1974. “An Index of Factor Simplicity.” Psychometrika 39: 31–36.
Karia, N., and C. Y. Wong. 2013. “The Impact of Logistics Resources on the
Performance of Malaysian Logistics Service Providers.” Production
Planning & Control 24 (7): 589–606.
Kelessidis, V. 2000. Benchmarking. INNOREGIO: dissemination of innovation
management and knowledge techniques, 1–33. Report produced for the
EC-funded project, Thessaloniki Technology Park.
Levine, T. R. 2015. “Confirmatory Factor Analysis.” In The International
Encyclopedia of Interpersonal Communication, edited by Charles R. Berger,
Michael E. Roloff, Steve R. Wilson, James Price Dillard, John Caughlin, and
Denise Solomon, 1–5. Hoboken, NJ: John Wiley & Sons, Inc.
Lockamy III, A., and K. McCormack. 2004. “Linking SCOR Planning Practices to
Supply Chain Performance: An Exploratory Study.” International Journal of
Operations & Production Management 24 (12): 1192–1218.
Lu, C.-S., and C.-C. Yang. 2010. “Logistics Service Capabilities and Firm
Performance of International Distribution Center Operators.” The Service
Industries Journal 30 (2): 281–298.
MacKerron, G. C., R. Masson, and M. McGlynn. 2003. “Self Assessment: Use
at Operational Level to Promote Continuous Improvement.” Production
Planning & Control 14 (1): 82–89.
Meybodi, M. Z. 2009. “Benchmarking Performance Measures in Traditional
and Just-in-Time Companies.” Benchmarking: An International Journal 16
(1): 88–102.
Moffett, S., K. Anderson-Gillespie, and R. McAdam. 2008. “Benchmarking
and Performance Measurement: A Statistical Analysis.” Benchmarking: An
International Journal 15 (4): 368–381.
Mundfrom, D. J., D. G. Shaw, and T. L. Ke. 2005. “Minimum Sample Size
Recommendations for Conducting Factor Analyses.” International Journal
of Testing. 5 (2): 159–168.
Neely, A. D., and Mike Gregory. 1995. “Performance Measurement System
Design.” International Journal of Operations & Production Management 15
(4): 80–116.
Negrão, L. L. L., M. G. Filho, and G. Marodin. 2017. “Lean Practices and Their
Effect on Performance: A Literature Review.” Production Planning & Control
28 (1): 33–56.
Niemi, P., and J. Huiskonen. 2008. “An Approach to Improving Logistical
Performance with Cross-unit Benchmarking.” Benchmarking: An
International Journal 15 (5): 618–629.
Panwar, A., B. P. Nepal, R. Jain, and A. P. S. Rathore. 2015. “On the Adoption of
Lean Manufacturing Principles in Process Industries.” Production Planning
& Control 26 (7): 564–587.
PRODUCTION PLANNING & CONTROL 629
Appendix
Figure A1. Process mapping with cycle time and steps for the ocean freight imports department.
630 A. BHATTACHARYA AND D. A. DAVID
Figure A2. Process mapping with cycle time and steps for the air freight exports department.
PRODUCTION PLANNING & CONTROL 631
Figure A3. Process mapping with cycle time and steps for the air freight imports department.
- Abstract
1. Introduction
2. Theoretical background
2.1. Internal benchmarking
2.2. Performance measures for the logistics industry
3. Operations of the target global logistics firm
3.1. The ocean freight division
3.2. The air freight division
3.3. Service scheduling approach
4. Materials and method
4.1. Instrument development
4.2. Data collection procedure
4.3. Sample criteria and design
4.4. Profile of the respondents
4.5. Scale development and data analysis
4.6. Application of relevant weights
4.7. Application of relevant sub-weights
4.8. Measurement of operational costs
4.9. Measurement of processes’ efficiency
4.10. Measurement of service quality
5. Results and analysis
5.1. Reliability analysis
5.2. Exploratory factor analysis
5.3. Application of weights
5.4. Actual performance measurement of efficiency of processes
5.5. Actual performance measurement of perceived quality
5.6. Actual performance measurement of cost effectiveness
5.7. The internal benchmarking tool
6. Discussion
6.1. Practical implications
6.2. Theoretical implications for logistics industry
7. Conclusions
7.1. Scope for future research
Acknowledgements
Disclosure statement
Notes on contributors
References