Assessing the impact of supply chain agility on operational performances-a PLS-SEM approach

Abstract

Purpose
This study aims to examine the effect of supply chain agility (SCA) on operational performance (OP) measurements of steel manufacturing firms. It also investigates the role of cost efficiencies concerning enhance OPs.

Design/methodology/approach
The study is based on an experimental research design by collecting data from responses 398 responses of key officials of India’s steel manufacturing firms. Analyses are carried to explore this modern concept with the help of Smart-partial least square (PLS) version 3.3.2 with confirmatory factor analysis and PLS structural equational modelling.

Findings
SCA factor (SCAF) directly has influenced the firm’s OP. It also represents cost efficiencies that have partial mediation between the SCAF and OP. The impact of cost efficiencies on OPs is strongly significant as compared to the impact of SCAF on cost efficiencies.

Practical implications
Management teams in the manufacturing industry should stress the role of SCA as a comprehensive concept in responding to market needs in a volatile environment. SCA reflects one of its winning strategies in today’s dynamic and competitive world. Managers must thoroughly know the ramifications of agility to develop a mechanism for determining the procedures and identifying inequality in SC operation.

Originality/value
This study speaks explicitly about the linkage between SCAF, OP, CE. It is an addition to the existing theories of RBV. Enhancements in OP measurements, specifically performance and flexibility, will lead to better firm performance. study conceptualizing the complementing effects of SCA (IS capability) and OPs and second cost efficiencies play positive partial mediating effect in between the link. The achievement of SC agile is especially a critical approach to Boost customer satisfaction and differentiate market position.

Full text

Assessing the impact of supply chain
agility on operational performances-a
PLS-SEM approach
Rashmi Ranjan Panigrahi, Duryodhan Jena, Jamini Ranjan Meher and
Avinash K. Shrivastava
Abstract
Purpose –This study aims to examine the effect of supply chain agility (SCA) on operational
performance (OP) measurements of steel manufacturing firms. It also investigates the role of cost
efficiencies concerning enhance OPs.
Design/methodology/approach –The study is based on an experimental research design by
collecting data from responses 398 responses of key officials of India’s steel manufacturing firms.
Analyses are carried to explore this modern concept with the help of Smart-partial least square (PLS)
version 3.3.2 with confirmatory factor analysis and PLS structural equational modelling.
Findings –SCA factor (SCAF) directly has influenced the firm’s OP. It also represents cost efficiencies
that have partial mediation between the SCAF and OP. The impact of cost efficiencies on OPs is strongly
significant as compared to the impact of SCAF on cost efficiencies.
Practical implications –Management teams in the manufacturing industry should stress the role of SCA asa
comprehensive concept in responding to market needs in a volatile environment. SCA reflects one of its winning
strategies in today’s dynamic and competitive world. Managers must thoroughly knowthe ramifications of agility
to develop a mechanism for determining the procedures and identifying inequality in SC operation.
Originality/value –This study speaks explicitly about the linkage between SCAF, OP, CE. It is an
addition to the existing theories of RBV. Enhancements in OP measurements, specifically performance
and flexibility, will lead to better firm performance. study conceptualizing the complementing effects of
SCA (IS capability) and OPs and second cost efficiencies play positive partial mediating effect in
between the link. The achievement of SC agile is especially a critical approach to Boost customer
satisfaction and differentiate market position.
Keywords Supply chain, Agility, Operational performance, Resource-based view,
Indian manufacturing firms, Cost efficiencies
Paper type Research paper
1. Introduction
Due to the rapidly growing competition in the network, supply chain agility (SCA) is at its
most critical phase (Golgeci et al.,2019). Information systems playing a significant role in
SCM, which enables achieving SCA (Gunasekaran et al.,2019;Yusuf et al.,2004). Few
studies also argued integration of information systems (IS) for improving performance is not
clearly explained (Fawcett and Magnan, 2002;Mabert et al., 2003;Wu, 2019). Supply chain
integration is defined as an enabler for integration strategy, which in turn contributing to the
firm’s operation (Devaraj et al.,2007;Gunasekaran et al.,2019;Wei et al.,2020). To boost
the operational effectiveness in customers satisfaction, in today’s intense marketplace has
taken on paramount importance (Stock and Seliger, 2016;Xu and Liu, 2017). Agile supply
chain management is essential for being responsive, agile and aware in the face of change
(“Agile Manufacturing-OptiProERP”, 2020;Kumar et al.,2019).
Rashmi Ranjan Panigrahi
and Duryodhan Jena are
both based at Faculty of
Management Sciences,
IBCS, Siksha ‘O’
Anusandhan, Deemed to
be University,
Bhubaneswar, India.
Jamini Ranjan Meher is
based at the IIM Indore,
Indore, India.
Avinash K. Shrivastava is
based at Department of
Management Information
System and Analytics,
International Management
Institute-Kolkata, Kolkata,
India.
Received 5 June 2021
Revised 17 October 2021
Accepted 12 December 2021
The authors declare that they
have not received any financial
support for the research,
authorship, and/or for the
publication of this article.
DOI 10.1108/MBE-06-2021-0073 ©Emerald Publishing Limited, ISSN 1368-3047 jMEASURING BUSINESS EXCELLENCE j

Supply chain traits including agility, adaptability and alignment are explained by using the
resource-based view (RBV) (Barney,1991, 2001). RBV focuses on resource heterogeneity,
allocation, independence, utilization and imitability to create competitive advantages. The
resource is a grouping of factors owned or controlled by a corporation (Bromiley and Rau,
2016;Madhani, 2012;Ringim et al., 2012). They suggested that resources that provide
competitive advantage can cross firm boundaries and be incorporated in inter-firm
linkages. External resources in relational networks can also provide competitive benefits
(Bell-Hassan, 2019;Dyer, 1996). Developing SCA is vital for enterprises to maintain a
competitive advantage and improve OP in today’s global market. This led to a shift in the
unit of analysis from SCA to Cost and then from cost to OP (Fawcett and Magnan, 2002;
Fawcett and Waller, 2015;Manzoor et al.,2021;Wu, 2019).
In today’s economy, response to customers is critical to success (Feizabadi et al.,2019;
Van Hoek et al.,2001). When it comes to agility, it is all about making that responsiveness
happen (C¸ alısır and Hanc¸ erlio
gullari, 2017). This indicates that supply networks should be
customer-oriented rather than forecast-driven if they are going to achieve SCA (Afshan and
Motwani, 2021;Gunasekaran et al.,2019). When it comes to a company’s success, using
technology is critical. Strategic SC management has proven to be an effective approach in
enhancing SSC performance. (Afshan and Motwani, 2021;Khan et al.,2019). As a further
objective of SCA, all resources like customer sensitivity, process integration, network
integration, virtual integration must be reconfigured quickly enough to deal with changes
and uncertainties (Hsu and Chang, 2021). An organization’s ability to respond quickly while
also being flexible is a major aspect of agility in the modern business environment
(Karada
g, 2018;Sriyanalugsana and Suwantararangsri, 2020). The agility of the supply
chain is not just for huge organizations but also for small- and medium-sized businesses
(SMEs) with low capital requirements (Khan et al., 2019;Nazempour et al.,2018;Tasdemir
and Hiziroglu, 2019).
Supply chain agility efforts should be integrated to help business operations (Frohlich and
Westbrook, 2002;Johnson, 1999;Lee et al.,1997;Sanders, 2007). The firm’s performance
can be defined using several aspects such as delivering, expense and performance
aspect. These aspects get a friendly connection with supply chain collaboration (Devaraj
et al., 2007). SCA also puts a significant impact on customer responsiveness (Lee, 2004),
flexibility(Goldman et al., 1995) and both are also being impacted by SC agility (Bernadette
Nambi Karuhanga, 2007).
This paper focuses on measuring the impact of SCA on OP, four dimensions were used, i.e
responsiveness, flexibility, dependability and delivery (De Meyer and Ferdows, 1991;
Nawanir et al.,2013;Oh et al., 2019;Slack et al., 2007). The report highlights Network
Integration, virtual integration, process integration, customer sensitivity as an aspect of SCA
contribute to OPs. Van Hoek et al. (2001) explain responsiveness in production and service
processes. Supply chain management requires dependability (Gunasekaran et al., 2008). It
strengthens the firm by focusing on its core skills (Christopher et al., 2004). Flexibility boosts
productivity in industrial and service industries. Flexible systems trump typical operating
processes in factories (Van Hoek et al., 2001). Delivery boosts sales, profitability or other
financial metrics (Williams and Naumann, 2011). Plant motion detection saves money
operations requiring little total input (Swink et al.,2005). agile manufacturing (AM) is a
clever technological firm that produces easily and is adaptable. To reduce manufacturing
costs, agile working organizations develop new management and worker abilities to
combine with computer technology. (V
azquez-Bustelo et al.,2007). An AM is a smart,
adaptable technical enterprise. Agile working organizations integrate management and
worker skills with computer technology to cut manufacturing costs (Devaraj et al.,2007;
Routroy et al., 2018b;Wheelen et al., 2015).
The present study found from earlier research highlighted the issues about agility and
environmental uncertainty in supply chains (Inman and Green, 2021), study on demand-
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and supply-side risk management methods and their impact on business performance
(Sturm et al., 2021), an analysis of SCA literature (Patel and Sambasivan, 2021), improving
supply chain performance through increased visibility, agility and cooperation (Baah et al.,
2021), a study of an Indian manufacturing organization’s agility control system (Singh Patel
et al.,2020
), Taguchi loss functions and experiment design for manufacturing SCA analysis
(Routroy et al., 2018a), agility matrix was having significant contribution in operation
management and leads achieve business excellence (Carvalho et al.,2019), supply chain
adaptation and alignment in the Indian auto component industry (Dubey et al.,2018).
Except for the last two studies, other remain silent on the impact of SCA on the OPs and
again SCA in the Engle IS integration, i.e. customer sensitivity, process integration, network
integration, virtual integration towards measuring impact on OPs of Indian manufacturing
firms has not being explored.
SCA is gaining importance as the unit of competitiveness is seen as a supply network
(Ismail and Sharifi, 2006;Sharifi et al., 2006). Despite its importance in supply chain
management (SCM), the method integrated SCA (ISCA) integration improves OP is unclear
(Gunasekaran and Ngai,2004a, 2004b;Mahidin et al.,2004). From the review, it is clear
that customer demand flexibility, adaptability, stability, delivery systems performances not
address, which increasing the importance of the study that how the above said four factors
of SCA helps to contribute the OP in the Indian context. In addition, rather than conducting
a study on SCA-OP, authors prefer to modify it through the lens of cost efficiencies. Study
purposes that, is there any mediation effect of cost efficiencies in between SCA and firms’
OP in Indian manufacturing units.
Moreover, the study contributes to the current body of research by empirically testing RBV
theory in the context of SCA (M. Gligor and Holcomb, 2014) and OPs in competitive ear for
Indian manufacturing firms. Specifically, the paper examines this theory to test the second-
order factor interaction effect on the relationship between SCA and OP in the presence of
mediation effect of cost efficiencies by studying survey data of 398 responses of key
officials of selected India’s steel manufacturing firms. Looking at objectives and recent
literature to test second-order factor analysis widely used method is partial least square
structural equational modelling (PLS-SEM) analyses (Ibarra-Cisneros and Hernandez-
Perlines, 2020). So, the present study was carried to explore this modern concept with the
help of Smart-PLS version 3.3.2 with confirmatory factor analysis and PLS-SEM. As a result
of this analysis, we offer managers concrete advice on decision-making regarding
operations performance and supply chain trade-offs (Dubey et al., 2018;Hallavo, 2015;
Irfan et al., 2019).
The first contribution is in conceptualizing the complementing effects of SCA (IS capability)
and OPs, and the second cost efficiencies play a positive partial mediating effect in
between the link. The relationship between SCA and OP in Indian steel manufacturing firms
has not been examined before and then the linking effect of cost efficiencies between SCA
and OP further boosted the concept of IS capability.
2. Conceptual framework and hypotheses development
2.1 Literature review
This study uses a methodical approach that includes a review of the literature. Articles on
the subject of interest and the repute of the journal source were collected and then selected
for inclusion in the study. A total of 175 papers were gathered, with over half of those, or 87
articles, being eliminated on the grounds outlined above (Figure 1), and the remainder of
the study focusing on only 88 articles. All articles were examined and divided into three
sections: SCA, cost efficiencies and OP. A comprehensive literature review (Denyer and
Tranfield, 2009;Schmeisser, 2013) was conducted to widen the scope of the identified
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peer-reviewed publications that focus on “Supply chain Agility on operational performance
through cost efficiencies”.
2.1.1 Supply chain agility Innovative technology and business methods have been explored
by businesses to maintain a competitive advantage while also attempting to create deeper
ties with suppliers and consumers to increase quality and flexibility in fulfilling growing
demands (Boone et al.,2007;Tse et al.,2016). In industries where product manufacture is
complex and extensively reliant on supply networks, as the automobile sector, the
relationship of companies with their suppliers will be more dependent on the supply chain
than usual. The unit of competition is therefore shifting from single firms to supplier chains
(Croxton et al.,2001;Jose and Shanmugam, 2019).
The agility of a company’s supply chain refers to its ability to respond swiftly to customer
needs while also keeping costs in check (Golgeci et al., 2019;Gunasekaran and Ngai,
2004b;Inman and Green, 2021;Wu, 2019). The creation of an agile supply chain is
currently a top priority for many well-known companies (Fisher, 1997). Manufacturing firms
ranging from raw material suppliers to manufacturers and merchants may be required to
participate in the process of building an agile supply chain.
Sanders (2007) highlights that successful firms have close collaboration with their partners,
enabling real-time information transfer across supply chains as well as coordinated inventory
management. This means that products can be delivered quickly and reliably (Gligor et al.,
2015). Hence, Devaraj et al. (2007) conclude that the dimensions of performance related to
aspects of delivery timing, cost and quality discovered by the customer have a strong
relationship with supplier integration. In addition to such operational advantages, SCA also
improves customer responsiveness (Christopher and Lee, 2004) and flexibility (Goldman et al.,
1995) by incorporating both (Pe
´rez-Salazar et al.,2017).
Figure 1 Methodology of literature review
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Swafford et al. (2000),(2006) argue that external firms’ agility is not included in the value
chain’s flexibility. After reading the next lines, you will have a clear understanding of how
(Van Hoek et al.,2001).
䊏Customer Sensitive: SCA is the capacity to respond swiftly to changes through mass
customization (Mason-Jones et al., 2000). (Mason-Jones et al., 2000;Sharifi et al.,
2006). To meet client expectations with short lead times, a supply chain must be agile
(Christopher and Lee, 2004).
䊏Process Integration: Process integration is focused on handling uncertainty and change (Van
Hoek et al.,2001
). Using flexible supply networks and information systems, businesses can
achieve greater levels of process integration (Gunasekaran et al.,2019;Yusuf et al.,2004) That
is, resources can be allocated flexibly within the supply chain to meet various industrial
objectives (Yusuf et al.,2014). It should also be able to enable concurrent rather than serial
business processes to meet the demands of various consumers (Goldman et al., 1995).
䊏Network Integration: Instead of a long-term partnership, network integration refers to
cooperating through “fluid” groupings of network associates (Van Hoek et al., 2001). In
today’s competitive marketplaces, the ability to use partner strengths and align supply
and demand under adequate cost control is the sustainable advantage (Barve, 2011).
Suppliers are encouraged to participate in corporate activities such as shared product
development, collaborative planning, common systems and vendor management
inventories (Agarwal et al., 2007;Kumar et al., 2019;Moeen and Agarwal, 2017).
䊏Virtual Integration: Virtual integration refers to exploiting supply chain data. It emphasizes the
value of information, especially information sharing and exchange (Van Hoek et al.,2001).
Virtual integration enables real-time asynchronous inter-firm planning and execution in
supplynetworks(
Maheswari and Kalyan, 2020;Saleheen et al.,2018). Firms can acquire
and handle enormous amounts of data to cooperate with supply chain partners by utilizing
information impact (Vickery et al., 2003). Enterprise resource planning (ERP) is a commonly
used technology in manufacturing to organize supply chain processes (Wagura, 2015). This
shift in the supply chain paradigm also affects inventory. Traditional logistic systems, for
example, rely on inventories (Boone et al., 2007).
2.1.1 Cost efficiencies. Theoretically, transaction costs are important when economic players
undertake relation-specific investments in uncertain settings (Williamson, 2010). Transaction
costs in the supply chain are mostly caused by external business environment uncertainties
(Piboonrungroj et al., 2011). To some extent, the influence of company trust on logistics
performance is mediated by lower transaction costs for enterprises that trade with one another
(Piboonrungroj et al., 2011;Yang et al., 2019).Cost-effectiveness is examined as a mediating
factor between SCA and a company’s performance (Yang, 2014). Investment in recurrent
transactions discourages efforts to obtain a private advantage, and transaction cost economics
indicates that trade participants might reduce uncertainty and conflict by investing in specialized
relationships (Williamson, 1987). Trading partners incur threshold expenses including setting up
connections, contracts and governance structures in a foreign and unfamiliar environment as
part of the transaction costs (Verwaal and Donkers, 2001). Because of the confidence,
dedication and cooperation engendered by an agile supply chain, manufacturers and their
suppliers are less likely to act opportunistically, resulting in lower transaction costs (Yang, 2014).
2.1.2 Operational performances Numerous quantitative and qualitative targets can be
defined to improve performance over the duration (Glaister et al., 2008;Greenley, 1994;
Wheelen et al.,2015). Because quantitative metrics are difficult to collect, it has been
proposed that qualitative measures should be incorporated in performance evaluations
(Chae et al., 2014). While the existing literature provides numerous dimensions of OP that
may apply to the SME environment, this study identified a list of seven OP indicators. These
performance objectives include reduced lead time in production, “accurate forecasting,”
“better resource planning,” “greater operational efficiency,” “reduced inventory level,” “cost
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savings,” and “more accurate costing.” On a five-point scale ranging from “certainly better”
to “about the same” to “worse” or “don’t know,” respondents were asked to indicate how
their business had done over the past three years in comparison to its significant
competitors on each of these OP criteria. OP is defined as “the development,
implementation, and application of performance measures at the level of day-to-day
operations” (De Leeuw and Van Den Berg, 2011). Bayraktar et al. (2009),Panigrahi et al.
(2019),Panigrahi et al. (2021a), Panigrahi et al. (2021b) suggested that it is difficult for
enterprises to choose a single OP measure. Nawanir et al. (2013) delivered on time and
budget while maintaining high levels of quality and inventory minimization (Devaraj et al.,
2007) used cost, quality, flexibility and delivery. Similarly, Hallgren and Olhager (2009)
claimed that manufacturing organizations’ key performance indicators were costs, quality,
time to market and adaptability. Furthermore, Leite and Braz (2016) asserted that the
indicators used to assess an organization’s OP under AM include the speed with which
products are delivered and how flexible the product offerings are (Nabass and Abdallah,
2018). OP cost, quality, delivery, flexibility and innovation were all taken into account. As a
result, there were numerous signs associated with OP in the literature. For instance,
(Negra
˜oet al.,2020) operations were assessed by looking at factors such as cost, quality,
delivery flexibility, new product development and the time it took to market for new items.
Responsive, dependability, delivery performances and flexibility are the most commonly
cited measures of OP in the literature, so they were included in our study as well.
Responsiveness is described as the ability to adjust production and service offerings (Van
Hoek et al., 2001). Ineffective supply chains require it (Holweg and Pil, 2008).
Flexibility is an important operational quality that has been extensively researched. An
analysis of product and volume flexibility. Product flexibility is the ability to manage tough,
non-standard demands (Vickery et al.,2003). Volume flexibility is the ability to modify
product volume during high demand and slack periods (Sanders, 2007;Slack et al., 2007).
Along with flexibility, supply chain dependability is vital. Cooperation among partners, using
their skills and focusing on the firm’s core competencies (C.R et al.,2019;Christopher et al.,
2004;Slack et al.,2007). Information acquisition is the process of gathering useful data
(Kohil et al., 1993), whereas information dissemination is the extent to which the data is
shared within the firm’s functional divisions (Neely et al., 1995). The study investigates the
impact of SCA on OP parameters such as responsiveness and dependability as well as
delivery and flexibility in the presence of cost efficiencies.
2.2 Theoretical framework and development of hypothesis
2.2.1 Theoretical framework This study takes an integrated theoretical approach that
integrates the theory of RBV and transaction cost economy. These theories affect business
operations and demonstrate why corporations expand operations beyond their target
destination (StudyCorgi, 2020). Both the resource-based approach and transaction cost
economics (Eike and Chu
¨tter, 2009) are significant theoretical frameworks in the
manufacturing and processing industry today. These theories stem from extensive
globalization studies and literature. Two theories are compatible but inform distinct
decisions: RBV theory shows how companies are increasingly relying on IT to optimize
supply chain processes (Liu et al.,2020)(Wu et al., 2006). IT-enabled integrated SCA
(ISCA) can help a company maximize its IT resources (Sharifi et al.,2006), whereas TCE
theory suggests threshold costs arise when trade parties must establish connections,
contracts and governance mechanisms in a foreign and unfamiliar setting (Verwaal and
Donkers, 2001). With an agile supply chain, manufacturers and suppliers have less
opportunism, which lowers transaction costs.
As a result, the study will look at cost-effectiveness as a mediator. This study, which is
based on RBV theory and transaction cost economics, uses an integrated theoretical
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approach to build a conceptual model that aims to identify the crucial factors of SCA and
justify the proposed mediating role of cost efficiency on OP.
2.2.2 Development of hypothesis
2.2.2.1 Supply chain agility factor and cost efficiencies. One of the highly cited SCA
frameworks is developed by Van Hoek (Van Hoek et al.,2001). They have explained the
framework from point of view of four dimensions of agility in the supply chain as proposed in
the article of Goldman et al. (1995). Dimension are enriching customers, cooperation for
improved competitiveness, an organization for adoptability change, people and IT.
Frameworks try to bring integration of the supply chain and its underlying principle to a
comprehensive-based approach (Goldman et al., 1995;Van Hoek et al.,2001). SCA
ensures responsiveness to customer demands, customer services, utilization of resources
and high business performance along with cost flexibility to enhance productivity, including
differentiation in the modern business environment (Agarwal et al.,2006;Katayama and
Bennett, 1999). In contemporary literature, the impact of SCA on cost performance is not
well studied. It has been shown that AM has no significant impact on cost-effectiveness
(Hallgren and Olhager, 2009;Swink et al.,2005). On the other part, it has been argued that
combining lean productivity with a versatile operating model helps agile businesses to
achieve superior performance at mass production costs (Adeleye and Yusuf, 2006).
Furthermore, AM focuses on advanced technologies that allow products to be produced
economically and suited to the needs (Wheelen et al., 2015). An agile supply chain can
strategically leverage its resources and turn costs through customer value, which besides
contributes to superior results (C.John Langley and Holcomb, 1992;Halley and Guilhon,
1997;Musau et al., 2017), and helps us to develop the hypothesis.
H1. SCAF exerts a direct effect on cost efficiencies.
2.2.2.2 Supply chain agility factor and operational performances. According to RBV theory,
in today’s global economy, firms must implement SCA and lean techniques to sustain and
improve their OP (Manzoor et al., 2021). According to RBV, organizations must continually
evolve and rebuild their capabilities to maintain a competitive advantage over the long term
(Teece et al., 2009). Internal and external competencies can be integrated, built and
reconfigured to address dynamically changing circumstances using dynamic capabilities
(Teece and Pisano, 1994). An organization’s ability to change operational procedures has
been demonstrated to be enhanced by SCA. It allows for resource reconfiguration and for
environmental dangers and opportunities to be sensed and capitalized on. Few kinds of
literature, a strong argument in favor of qualitative measures for OPs used to measure
supply chain factors (Chakravarthy, 1986;Wheelen et al., 2015). The literature explains
indicators to measure operational efficacy are “reduction of lead time in production, more
accurate costing, and resource planning”. These are rated on a five-point Likert scale from
definitely better to worse (Bayraktar et al.,2009;Panigrahi et al.,2019;Panigrahi et al.,
2021a;Panigrahi et al., 2021b). Organizational learning and innovation are also linked with
performance measurement (Neely, 2004;Wu, 2019). Likewise, some of the studies were
found to indicate that companies are measuring service performance based on multiple
indicators rather than on a single one. Four dimensions of measuring performance are
responsiveness, flexibility, quality, delivery speed is frequently and widely used in
the supply chain management context (Sanders, 2007). Only a few studies have looked at
the relationship between SCA and firm performance. But the relationship between SCAF
and Fop in the Indian industry has not been explored. When describing how agility adds
value, traditional performance metrics might be used. To allocate resources effectively,
companies must have a comprehensive grasp of the performance benefits of SCA. Based
on the arguments, research proposed the hypothesis as:
H2. SCAF exerts a direct effect on OPs.
2.2.2.3 Cost efficiencies and operational performances. Cost efficiency exerts a direct
positive effect on the manufacturer’s performance (Yang, 2014). Cost efficiency is the
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demonstrated ability to execute plant operations using relatively few total input resources
(Swink et al., 2005). SCA may reduce cost efficiency, as it involves more investments to
accomplish increasing ability to customize products, adjust production volumes, respond to
changes in delivery performance and produce a range of products. The collaboration
between manufacturers and their suppliers fostered by SCA allows for transactions
economizing on bounded rationality and safeguarding against the hazards of opportunism
(Lai, 2009); thus, the transaction costs and total input resources are reduced. As the
emerging economy’s society stresses higher relational harmony at the individual, group and
society levels compared with Western societies (Bruton and Lau, 2008). Thus, we posit that:
H3. Cost efficiencies have a direct impact on performances.
2.2.2.4 The mediating role of cost efficiencies in between supply chain agility and opera-
tional performance. Transaction cost economics theory guides this research, which looks at the
mediating role of cost efficiency between a firm’s SCA and its OP. According to transaction cost
economics, trade parties can reduce uncertainty and conflict by investing in recurring
transactions that are distinctive to their relationships(Verwaal and Donkers, 2001). Thus, such
investments deter individuals from trying to obtain an unfair advantage through private
transactions (Luo et al., 2009). Creating contacts, contracts and governance frameworks in a
new and remote context incurs threshold costs as part of the transaction costs associated with
commercial exchanges (Williamson et al., 2006;Williamson, 2010;Verwaal and Donkers, 2001).
An agile supply chain fosters long-term relationships based on mutual trust, commitment and
cooperation, which reduces opportunism and, as a result, transaction costs for manufacturers
and suppliers (Bylund, 2015). As a result, the study will look at cost-effectiveness as a mediator.
Informed by RBV theory and transaction cost economics, this research uses an integrated
theoretical approach to build a conceptual model that aims to identify the key factors of SCA
and justify the proposed mediating role of cost efficiency among the relationship in between
SCA and OP. An agile supply chain may efficiently allocate resources and convert costs into
customer value, resulting in greater performance (Irfan et al.,2019).
Hence, the hypothesis was proposed:
H4. Cost efficiencies have mediation effect in between SCA and OP.
The study conceptualizes the research model by taking into four proposed hypotheses in
Figure 2.
3. Methodological foundation
3.1 Sample selection
The population size of this report, consisting of four steel manufacturing units, namely, Steel
Authority of India Limited (SAIL), Jindal Steel, Rashmi Group, MESCO steel, was 4600
personnel. The preference of these manufacturing firms is based on ease of accessibility
Figure 2 Conceptual model
SCAF
CE
H3
H1
H2OP
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concerning geographical proximity in India. Targeted respondents are key steel production
firms officials, i.e. production manager, operations manager, quality control manager,
marketing manager.
Out of the total population, we have identified the required sample size for our study in the
following manner. For survey 623, no questionnaires were distributed and received 488
responses, out of that 398 responses were found to be valid after data screening. The
sample size is determined by the use of Gpower software to identify the minimum required
sample size (Faul et al., 2009).
The power of the test was used at 0.99 for obtaining a sample size of 357 respondents but
in our research, we have taken total sample responses of 398, which satisfy minimum
sample size requirement criteria. A total of 398 correct responses are used for the final
analysis of PLS-SEM. These modeling techniques are run through the use of SmartPLS
Software software 3.3.2 version. PLS-SEM through Smart-PLS provides greater flexibility to
handle the complex relationship model (Hair et al.,2016). PLS-SEM is more powerful
concerning prediction and handling the higher-order construct (Hair et al.,2017). It is also
used for the predictive relevance of the model (Sarstedt et al., 2014).
Table 1 shows the identities and designations of the participants, as they pertain to their
company. It is inferred from the figure that the SAIL has a maximum number of respondents
and the highest number of respondents from the classification are of production managers.
In this study, the researcher considered the manufacturing operation, so it is evident that
the majority of the respondents are chosen as manufacturing and operating managers
followed by production and other managers.
3.2 Survey instrument
The present study was carried out in steel manufacturing firms in India. Due to the shutdown
enforced during the period from March to June 2020, data are collected through an online
questionnaire. The data are collected during the pandemic period. The questionnaire was
distributed using google Forms. To distribute the questionnaire on a referral basis we have
taken 4 months. However, the responses were recorded for the 8 months. An online survey
was carried out using a non-probability convenient sampling approach to collect data
through a questionnaire as described in Annex (Table A1). The questionnaire was adapted
from the 15 items explored by Van Hoek et al. (2001),Naylor et al. (1999),Wu (2019) for
constructs 1: SCA factor [customer sensitivity, process integration, network integration,
virtual integration], 15 items explored for constructs 2: operational performance study
adopted the scale of De Meyer and Ferdows (1991),Nawanir et al. (2013),Slack et al.
(2007) and 5 items are considered for cost efficiencies constructs, which was adopted (De
Meyer and Ferdows, 1991;Nawanir et al.,2013;Slack et al., 2007;Yang, 2014). All items
were measured on a five-point scale ranging from 1 to 5, representing strongly disagree to
Table 1 Distribution of sample respondents
Particulars Category No of Respondents (%)
Organization name SAIL 160 40.20
Jindal 97 24.37
Rashmi group 69 17.34
Mesco 72 18.09
Designation Operation manager 180 45.23
Production manager 125 31.41
Quality control manager 45 11.31
Marketing manager 48 12.06
Total 398 100.00
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strongly agree. A five-point scale is fully labeled if the population of the study is specific
(Weijters et al.,2010).
The collected data were screened by discarding the unengaged responses, missing
values, etc. The researchers have used adopted the established scale for the questionnaire
development. For the adopted scale, we have used the reliability and validity measure
which signifies the revalidation and authentication of the scale for the study. For the
response bias test researchers have used the VIF value. The VIF value in the analysis is less
than 3. This confirms that there is an absence of responsive bias in this study (Kock, 2015).
4. Results
This study has opted for an experimental approach to research. The analysis has been
done using SMRT PLS-3.2. Using SMART PLS, the measurement of the outer model and
inner model has been confirmed. The structural model has been conducted using 5000
bootstraps. Initially, Harman’s single-factor method has been adopted to investigate the
common method bias. In this method, all the items were loaded into a single factor and
found the variance explained is only 22% which is below the maximum threshold limit of
50% of the variance. Hence, it is confirmed that there is no common method bias is present
in this study.
4.1 Measurement model assessment
This study applying the composite reliability, convergent validity and discriminant validity to
investigate the outer model which signifies the confirmatory factor analysis results
(Schuberth et al.,2018). The constructs are such as SCA factor (SCAF), cost efficiency (CE)
and operational performance (OP) The CE was investigated through first-order reflective
approaches. The SCA factor was investigated by second-order reflective –reflective
measurement assessment. The OP component was also investigated through the second-
order reflective-reflective approach.
In the first stage of the two-stage R-R(reflective –reflective) approach, the latent variables
(SCAF and OP) of all four dimensions were assessed and then the scores of first-order
constructs were applied to the second-order construct in a reflective approach. Therefore,
second-order construct SCAF is designed to investigate the impact on another second-
order construct OP.
Initially, all the internal reliability was investigated through Cronbach’s alpha, Dijkstra and
Hensler’s Rho. However, Cronbach’s alpha for all the constructs was above the threshold
limit, i.e. 0.70 and the value of rho A is also meet the threshold limit, i.e. 0.70 (Hair et al.,
2020). Hence the construct reliability is confirmed. The convergent validity was also
examined through the AVE value. The AVE for all the construct abstracted had met the
minimum threshold value, i.e. 0.50 (Fornell and Larcker, 1981;Hair et al.,2018). Internal
reliability and convergent validity assessment are explained in Table 2.
This study also has gone through the discriminant validity assessment. The discriminant
validity was investigated by comparing the inter-item correlation. The under the root of AVEs
of the construct on the diagonal was higher than the inter-item correlation values (Fornell
and Larcker, 1981). The assessment of discriminant validity is shown in Table 3.
In addition to this, this study has gone through a new approach to analyzing the
discriminant validity. The heterotrait–monotrait (HTMT) ratio of correlation must be less than
one; however. the maximum HTMT ratio should be 0.85 (Henseler et al., 2015). The HTMT
ratio has been shown in Table 4.
Tables 3 and 4are sufficient enough to fulfill the criterion for discriminant validity. Thus, the
discriminant validity is confirmed.
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4.2 Structural model assessment
The relationship between the constructs has been investigated through structural model
assessment (Hair et al.,2018). This process is based on the developed hypothesis and
mediation analysis. The mediation analysis was used to follow the bootstrapping method
Table 2 Quality criterion for model assessment
Constructs Items Type Loadings/ Weights Cronbach’s alpha rho A CR AVE
Network integration NI1 Reflective 0.809 0.847 0.866 0.818 0.575
NI2 0.656
NI3 0.884
NI4 0.594
NI5 0.728
NI6 0.835
Virtual integration (VI) VI1 Reflective 0.788 0.737 0.749 0.835 0.559
VI2 0.648
VI3 0.76
VI4 0.787
Process integration (PI) PI1 Reflective 0.879 0.736 0.794 0.847 0.65
PI2 0.694
PI3 0.835
Customer sensitivity (CS) CS1 Reflective 0.956 0.81 0.979 0.908 0.832
CS2 0.866
Cost efficiencies (CE) CE1 Reflective 0.779 0.877 0.883 0.911 0.672
CE2 0.802
CE3 0.856
CE4 0.767
CE5 0.888
Responsiveness (RN) RN1 Reflective 0.834 0.69 0.71 0.828 0.617
RN2 0.816
RN3 0.699
Dependability (DL) DL1 Reflective 0.814 0.833 0.862 0.89 0.672
DL2 0.874
DL3 0.915
DL4 0.654
Flexibility (FL) FL1 Reflective 0.783 0.83 0.835 0.881 0.598
FL2 0.726
FL3 0.798
FL4 0.7
FL5 0.851
Delivery performance (DP) DP1 Reflective 0.796 0.707 0.715 0.69 0.556
DP2 0.726
DP3 0.713
Table 3 Fornell–Larcker approach for discriminant validity
CE CS DL DP FL NI PI RN VI
CE 0.672
CS 0.082 0.912
DL 0.221 0.075 0.819
DP 0.012 0.032 0.126 0.745
FL 0.451 0.164 0.204 0.062 0.773
NI 0.379 0.117 0.271 0.037 0.287 0.758
PI 0.084 0.051 0.05 0.005 0.044 0.113 0.806
RN 0.118 0.201 0.097 0.029 0.144 0.09 0.182 0.785
VI 0.128 0.187 0.093 0.123 0.163 0.196 0.116 0.058 0.747
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with recommended 5000 bootstraps to find out the required p-values for the analysis (Hair
et al.,2019
). In the structural inner model, all the predictors accessed and found the
variance inflation factor (VIF) is to be less than 5 (Jos et al.,2014). So no multicollinearity
was found in this study. The next step is for checking the significance and relevance of the
path coefficients. In this structural model, the SCA factor and OP are the 2nd order
reflective-reflective model.
As all the constructs are reflective, the study is qualified for the consistent PLS algorithm
(Dijkstra and Henseler, 2015). The model fit indices follow the SRMR value, as it is one of the
finest indices to evaluate the model fit (Hair et al.,2020). This study found the SRMR value
to be 0.056 which is below the threshold value of 0.08 and signifies that the model has good
explanatory power (Hair et al.,2019). Further, this study investigates the effects of
independent variables over the dependent variable along with the mediation analysis.
The results reveal that SCAF is an important predictor for OP having a standardized beta
value
b
=0.455,p<0.001. It signifies that H1 is supported. The second important predictor
for OP is CE with having standardized beta value
b
=0.711,p<0.001. It concludes that H2
is also supported by the study. To examine the effects of SCAF on CE, it is found that the
standardized beta value
b
=0.55,p<0.001, so H3 is also supported by the analysis.
Again the mediation analysis was conducted for the study. For mediation analysis, the direct
effect, indirect effect and total effects have been calculated. The variance accounted for (VAF)
is the most suitable for investigating the mediation effect. For mediation effect, the VAF should
be more than 0.2 <VAF <0.8 (partial mediation), VAF >0.8 (full mediation) (Hair et al.,2019;
Sarstedt et al., 2014). The VAF value is calculated by using the following formula.
VAF ¼Indirect effect
Total Effect
The calculated VAF is found to be 0.462. It signifies that there is a partial mediation effect in the
model in Table 5. The structural model assessment and hypothesis were described in Table 6.
5. Managerial implications
Management teams in the manufacturing industry should stress the role of SCA as a
comprehensive concept in responding to market needs in a volatile environment. SCA
Table 4 HTMT approach for discriminant validity
CE CS DL DP FL NI PI RN
CE
CS 0.095
DL 0.263 0.093
DP 0.098 0.101 0.194
FL 0.522 0.2 0.237 0.113
NI 0.429 0.139 0.343 0.177 0.342
PI 0.123 0.066 0.112 0.169 0.114 0.187
RN 0.153 0.266 0.134 0.102 0.194 0.134 0.275
VI 0.194 0.221 0.138 0.255 0.199 0.244 0.182 0.165
Table 5 Indirect, direct and total effect
Relationship Direct effects on OP Indirect effects on OP Total effects VAF
SCAF !OP 0.455 0.391 0.845 0.462
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reflects one of its winning strategies in today’s dynamic and competitive world. Managers
must thoroughly know the ramifications of agility to develop a robust conceptual model
mechanism for determining the procedures to follow and identifying any inequalities
through operation. Managers in manufacturing firms can improve performance in terms of
quality, delivery and flexibility by customizing their processes and systems to AM.
Enhancements in OP measurements, specifically performance and flexibility, will lead to
better firm performance. The achievement of SC agile is especially an important approach
to boost customer satisfaction and differentiate market position. The findings showed that
an agile SC enhances market efficiency by scaling up client satisfaction and differentiation,
and not eliminating benefits on an extremely competitive and individually tailored market. It
mandates that management extend their actions much beyond developing IT capability
and effective sharing of information with distributors to touch the supply chain strength.
6. Discussion
This study examined the effects of customer sensitivity, process integration, network
integration, virtual integration as indicators of SCA on cost efficiency and in turn affects the
OPs of steel manufacturing firms, Paper argue that built on RVB-based theory, IT-enabled
factors affect modern supply agility dimension and majorly contribute operational
excellence of Indian industry. We posited that these SCA IT-enabled resources complement
one another for providing agility to the manufacturing firms (Afshan and Motwani, 2021;
Chae et al.,2014;Gunasekaran et al.,2019). According to the RBV literature, manufacturers
do not operate at isolated operations from the majority of the supply chain. Whether
designing an agile capability or a lean production system may depend on where
participants are positioned in the production process. Supply chain agility can be improved
by integrating information systems; however, SCA is from the four SCA dimensions.
Integration of customers’ sensitivity in the supply chain’s agility, as well as the network’s and
virtual integration. There are significant operational advantages from IS-enabled SCA, such
as increased working efficiency, better information visibility, lower inventory levels, shorter
response times and more accurate forecasts This suggests that SCA offered by an IS may
be capable of providing customized services. Our findings in support the previous studies
on manufacturing firms (Chae et al., 2014). Agile producers can also expect higher
operational and firm performance, according to the research. V
azquez-Bustelo et al. (2007)
also discovered that adopting agile manufacturing has a beneficial impact on
manufacturing strength, resulting in increased manufacturing OP.
This study aims to check the impact of the SCA factor on the OPs of manufacturing firms.
Supply chain agility is positive and significantly affects the OP of manufacturing in selected
units; it is also evident that the cost efficiencies are playing the role of mediator between
SCAF and OP. These findings are following earlier studies (Nabass and Abdallah, 2018)
where agile manufacturing has a significant impact on OP but OP is positioning as a
mediator, our study slightly differentiate from that study concerning cost efficiencies, which
have played the role of mediator in between SCAF and OP which is extracted from the
literature of Nawanir et al. (2013),Yang (2014). In our study, we asked participants to
compare their costs to their transactions and competition for smooth supply chain
Table 6 Hypothesis table
Hypothesis Relationship t-value p-value Decision
H1 SCAF exerts a significant effect on cost efficiency 8.087 0.001 Supported
H2 SCAF has significant effects on OP 4.80 0.001 Supported
H3 Cost efficiency has significant effects on OP 9.58 0.001 Supported
H4 Cost efficiency has a mediating effect between SCAF and OP 6.133 0.001 Partial mediation supported
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performance. It revealed from analysis firms operating in more dynamic contexts might
expect a higher association between SCA and cost compared to their competitors. This is a
significant finding for managers. It asserts that SCA can cost stable enterprises money. In
essence, cost and agility are mutually compatible for stable firms, which is also proved in
the study of Chinese manufacturing firms (Yang, 2014). Agile strategies are the best fit
when operating in highly uncertain environments, thus finding is at par with earlier studies of
SCA on performances (Gligor et al., 2015).
The paper also explains in line with two established theories, i.e RBV theory and TEC theory.
Where its first contribution to RBV’s theories is customer sensitivity and process integration,
which also assists manufacturing companies to achieve competitive advantages in
achieving long-term results. It is one step ahead of a previous study (Um, 2017). Second, it
also touches on the extended theory of RBV, i.e. capability theory, in which, the contribution
of adaptive capabilities to respond to future changes. For modern competitive business,
fast-changing environment firms must extend their supply chain in the respect of network
integration (Holweg and Pil, 2008;White et al.,2007). Theory can be best fitted to the
improvement of OP through flexibility and the right delivery strategy.
Third and finally, this study’s results also contribute to the field of transaction cost
economics theory, which justifies the mediating influence of cost. Using the RBV and
transaction cost economics as a framework, this empirical study examines the antecedents
of a firm’s SCA and how cost efficiency mediates between agility and performance during
an economic exchange in transition economies. This research illuminates a previously
unnoticed link between RBV theory and transacts cost economics.
7. Limitation and future scope of research
At first, the authors identified some flaws in this study that hampered the interpretation of the
findings, and those will be left for future research. The study is limited to “Impact SCA (IS
capability) on OPs and cost efficiencies play mediating effect in between the link”. When
testing the study model and hypotheses, we started with cross-sectional data that showed
how manufacturing executives saw the world in 2020–2021. Cross-sectional data failed to
represent the constant improvement of the manufacturers’ IS capabilities, as well as their
attainment of SCA, cost efficiency and efficiency. This research may benefit from a follow-
up longitudinal study to determine how and why IS capability and operational collaboration
are linked to agility and cost-effectiveness and how they can play a mediating role in time.
This would be a worthwhile attempt.
Second, this research only looked at the connections between a few manufacturing-specific
information-sharing factors. This study could be furthered by incorporating other important
theoretical components. To better understand the relationship between SCA and intra- and
inter-organizational system adoption, it might be worthwhile to include some variables
related to these topics.
As it is a dynamic process, more study is required to solve the issue of information sharing
continuity. Long-term impacts of knowledge sharing in an economic exchange remain
elusive. Other industries and economies with diverse institutional characteristics and social
cultures, which have a significant impact on the buyer–supplier relationship, are urged to
expand the scope of the research.
Third, the lack of clarity surrounding the definition and measurement of a company’s
SCA necessitates additional study. This research focuses on a firm’s ability to
personalize products, adapt production volumes, respond to changes in delivery
performance and generate a wide range of goods for SCA. A firm’s ability to achieve a
given level of agility quickly enough while also being cost-effective is not addressed.
These aspects may be taken into account in future studies on flexibility and agility. Last
but not least, the study’s data was gathered from Chinese firms, which may have cultural
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variances from their western counterparts. Aiming to decrease cultural differences by
researching in Shanghai, China (a world-class business hub), we cannot presume that
these findings will hold in organizations with modern business culture. If the
manufacturers’ SCA is to be improved, future research should look at the cultural factor
(i.e. individualist culture). This study’s empirical evidence comes from China. Future
research should investigate how the deployment of a knowledge management strategy
affects the agility and performance of a manufacturer’s supply chain in different
economies.
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Appendix
Table A1 Adopted scales for SCA-CE-OP
Codes Used Constructs Used Sources
Types of Scale
Used
SCA Constructs 1: Supply Chain Agility Factor Scale Used 5 point
Likert 1 = Fully
Disagree, 5= Fully
Agree
CS Customer Sensitivity (CS) Naylor et al. (1999),Wu
(2019)CS 1 Pro-actively seeking new emerging markets
CS 2 Customer treated individually
PI Process Integration (PI) Naylor et al. (1999),Wu
(2019)PI 1 Mobility of resources to meet different
requirements
PI 2 Being nimble its processes to achieve different
objectives within the same facilities
PI 3 Being cost-effective re-configure to respond to
new production model
NI Network Integration (NI) Naylor et al. (1999 Wu
(2019)NI 1 Taking advantage of markets changes as
opportunities
NI 2 Having adaptive capabilities to be able to
respond to future changes
NI 3 The ability to meet customer changes as a
source of competitive advantages
NI 4 Suppliers’ involvement in the business
NI 5 Fast response to changes in supply
NI 6 Response to variations in demand quickly
VI Virtual Integration (IV) Van Hoek et al. (2001) ;
Wu (2019)VI 1 Leveraging information to understand market
and customer requirements
VI 2 Leveraging information to master organisational
changes
VI 3 Leveraging information to facilitate collations
with partners
VI 4 Our organization has the appropriate
technology and technological capabilities to
quickly respond to changes in customer
demand
OP Constructs 2: Operational performance Scale Used 5 point
Likert 1 = Fully
Disagree, 5= Fully
Agree
RN Responsiveness Ferdows and de Meyer
1990; Slack et al., 2007RN 1 Response to changes in the product due to
market uncertainty
RN 2 Process of demands from downstream
RN 3 Process of demands from upstream
DEP Dependability Ferdows and de Meyer
1990; Slack et al., 2007DEP 1 Leverage partners’ capability
DEP 2 Focus on core competence
DEP 3 A single supplier for each sourced product
DEP 4 Supplier-collaborative product design
FL Flexibility Ferdows and de Meyer
1990; Slack et al., 2007FL 1 Ability to handle difficult or non-standard orders
FL 2 The flexibility of increasing or decreasing
product effectively
FL 3 Our organization has high flexibility to change
product mix
FL 4 Our organization has high flexibility to change
the volume
(continued)
jMEASURING BUSINESS EXCELLENCE j

Corresponding author
Avinash K Shrivastava can be contacted at: kavinash1987@gmail.com
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Table A1
Codes Used Constructs Used Sources
Types of Scale
Used
FL 5 Our organization can introduce new products
into production quickly
DP Delivery performance Ferdows and de Meyer
1990; Slack et al., 2007;
Gunasekaran et al.,
2008;Nawanir et al.,
2013;Yang, 2014
DP 1 Our organization has on-time delivery
performance
DP 2 Our organization is capable of delivering
products to the market faster than its
competitors
DP 3 Our organization has fast delivery
CE Constructs 3: Cost Efficiencies Ferdows and de Meyer
(1990); Slack et al.
(2007),Nawanir et al.
(2013);Yang (2014)
Scale Used 5 point
Likert 1 = Fully
Disagree, 5= Fully
Agree
CE 1 Our unit manufacturing cost has decreased
during the last three years
CE 2 Our unit manufacturing cost is lower than our
competitors
CE 3 Market share as compared to your major
industrial competitors
CE 4 Return on assets as compared to your major
industrial competitors
CE 5 Average selling price(higher performance
means higher average price) as compared to
your major industrial competitors
jMEASURING BUSINESS EXCELLENCE j