Particularities of Balanced Scorecard frameworks relating to specific supply chain
roles
Antônio André Cunha Callado [email protected]
Área Temática: A6) Planejamento e Controlo de Gestão
Palavras-chave: Balanced Scorecard. Supply chain performance. Agribusiness.
Metodologia: M7) Survey
Particularities of Balanced Scorecard frameworks relating to specific supply chain
roles
Abstract The objective of this article is to identify particularities of Balanced Scorecard frameworks
relating to specific supply chain roles. Four independent samples of Brazilian input
suppliers, producers, distributors and retailers were formed. Each company was asked to
indicate which performance indicators they used through a questionnaire which presented a
list composed by forty-nine performance indicators divided into the four traditional
perspectives. Percentages were used to identify the performance indicators use patterns and
two value references (upper quartiles and estimated percentages) were used to identify the
performance indicators’ set for the supply chain roles considered. According to the results
presented, the group of eligible performance indicators relating to Balanced Scorecard
frameworks for specific supply chain roles changes its composition significantly as
different selection criteria area applied. The results also suggest that the Balanced
Scorecard approach for supply chain performance measurement should consider both
common and specific metrics for supply chain roles.
Keywords: Balanced Scorecard. Supply chain performance. Agribusiness.
1. Introduction
A supply chain is understood as a network of individual companies through which
products, money and information flows from input suppliers to end consumers and these
individual companies may play different roles in it according to their respective positions
into the supply chain structure.
Metrics are defined by Melniket et al (2004) as a verifiable measure, stated in either
quantitative or qualitative terms defined with respect to a specific point as well as are
consistent with values delivered to customers in a meaningful way. Performance indicators
are metrics expected to express quantitatively the effectiveness or efficiency or both, of a
part of or a whole process, or system, against a given norm or target (LOHMAN ET AL,
2004).
Performance indicators also have made a significant impact upon the performance
measurement literature, with a plethora of evermore complex framework models being
developed in many fields since the late eighties (FOLAN AND BROWNE, 2005).
According to Berry et at (2009), three approaches for performance measurement
and management have emerged from the literature: Kaplan and Norton’s Balanced
Scorecard, Simons’Lever of Control and Ferreira and Otley’s Framework. However, the
Balanced Scorecard approach can provide a suitable basis for performance measurement
into the supply chain context (BREWER AND SPEH, 2000).
Chenhall (2005) points out that, despite the use of the Balanced Scorecard
approach, there is still little evidence from surveys to support practical issues regarding to
key aspects such as the characteristics of the models tested, the information generated or
the combinations of metrics used. Furthermore, Abu-Suleiman et al (2003) highlight some
limitations of Balanced Scorecard frameworks designed for supply chain performance
measurement because of its top down approach, the lack of formal implementation
methodology and subjectivity of performance indicators’ selection.
The identification of the appropriate set of metrics to be applied by multiple
individual companies across a supply chain structure is not an easy task and there is little
literature about measurement selection methodologies (CHAN ET AL, 2003) and the
design of specific approaches addressed to this issue could provide a significant
contribution to this field of study (LAMBERT AND POHLEN, 2001).
The objective of this article is to identify particularities of Balanced Scorecard
frameworks relating to specific supply chain roles. This research is sponsored by the
Coordination of Improvement of Higher Education Personnel (CAPES).
2. Literature Review
The literature about supply chain performance measurement has increased
dramatically for the last two decades and efforts have been addressed to improve
performance measurement methods, the selection process of relevant metrics (MELNYK
ET AL, 2004). Beamon (1998) points out that the identification of suitable performance
indicators, as well as the existence among them, are sources of concerns. The Balanced
Scorecard approach has been used and several previous studies can be found.
Brewer and Speh (2000) examine how the traditional perspectives of the Balanced
Scorecard can be used to develop a framework for assessing supply chain performance,
providing a metric selection process that can be adapted for the supply chain context. They
conclude stating that key supply chain processes and interactions should be addressed.
Kleijnen and Smits (2003) investigate multiple metrics in supply chain management
through the Balanced Scorecard considering three traditional perspectives (financial,
customer and internal processes) and innovation as the fourth perspective to run forecast
simulations addressed attention to bullwhip effect, fill rate values and train users. They
conclude presenting a possible research agenda in supply chain management.
Savaris and Voltolini (2004) propose a methodology for the design of a Supply
Chain Scorecard structured by non-traditional perspectives. They conclude stating that this
model should be considered as a starting point and further research should be carried out.
Park et al (2005) proposed a framework for the Balanced Supply Chain Scorecard,
identifying a set of performance indicators considering the four traditional perspectives
though a survey. They conclude mentioning the relations between the framework and
expectations regarding to strategic enterprise management.
Bhagwat and Sharma (2007) develop a Balanced Scorecard for supply chain
management considering metrics from the four traditional perspectives. They state that the
framework proposed can be the foundation for a strategic supply chain management
system, but further research should be addressed to analyze the set of performance
indicators.
Zago et al (2008) apply a Balanced Scorecard framework to evaluate the
performance of a small liquor distributor company using non-traditional perspectives
composed by performance indicators from IMAM. They conclude stating that the
framework addresses specific attention to logistics activities and it could be adapted for
companies from different sizes.
Varma et al (2008) suggest a method to evaluate supply chain performance using a
combined approach of Analytical Hierarchy Process and the four traditional perspectives
of the Balanced Scorecard. They conclude stating that the four perspectives possess
different levels of importance with respect to the supply chain investigated.
Chang (2009) attempts to integrate supply chain management and Balanced
Scorecard considering supply chain traditional structure through a framework proposition
stating that there is a positive correlation between supply chain integration and each
dimension of the Balanced Scorecard.
Thakkar et al (2009) propose an integrated supply chain performance measurement
framework for small and medium enterprises through the traditional Balanced Scorecard
approach. They conclude stating that a set of performance measurement could be used for
supply chain evaluation.
Chia et al (2009) examine what supply chain senior executives measure and how
they perceive performance measurement from a traditional Balanced Scorecard
perspective. They conclude stating that main supply chain managerial focus is addressed to
customer satisfaction.
Bigliardi and Bottani (2010) developed a Balanced Scorecard model designed and
delimited for performance measurement in the food supply chain using the traditional
perspectives. They identified similar view from the companies investigated about three of
the four perspectives and divergent results relating to the learning and growth perspective.
3. Methodology
In order to achieve the objective proposed, several operational procedures were
performed. Firstly, a sample of agribusiness individual companies was assembled.
According to Gil (1996), in order to obtain significant and relevant data, the sample must
be composed by an adequate amount of elements. Silver (2000) goes even further stating
that samples with at least 30 elements should be used to assure proper statistical testing
designed to investigate any given characteristic. The sample used in this research was
composed by 121 agribusiness individual companies.
Secondly, two groups of variables were used. The first group was composed by
supply chain roles. Four supply chain roles were considered: Input suppliers, producers,
distributors and retailers. The second group of variables was composed by performance
indicators use patterns. presented in Beamon (1998), Rafele (2004), Gunasekaran,
McGauchey and Patel (2004) and Callado, Callado and Almeida (2009) were classified
among the four perspectives of the BSC, as follows:
• Financial perspective ⇒ profitability, liquidity, revenues by product, revenue
per employee, contribution margin, level of indebtedness, return over
investment, unit cost, minimizing costs, profit maximization, inventory, overall
earnings and operation costs.
• Customer perspective ⇒ customer satisfaction, customer loyalty, new
customers, market share, brand value, profitability by customer, revenue per
customer, business partners satisfaction, delivery time, responsiveness to
clients, growth in market share and maximizing sales;
• Internal processes perspective ⇒ new products, new processes, productivity by
business unit, products turnover, after sales, operational cycle, suppliers, waste,
flexibility, response time to customers, delay in delivery, response of suppliers,
storage time and information and integration of materials;
• Learning and growth perspective ⇒ investment in training, technology
investment, investment in information system, employee motivation, employee
capability, managerial efficiency, employee satisfaction, innovation
management, number of complains and risk management.
All individual company was asked to declare their respective role in supply chain as
well as to identify which performance indicators they use to performance control.
Thirdly, data collection was carried out by structured interviews through the use of
a questionnaire in which all variables were listed and senior managers from the individual
companies should indicate whether they used them or not. This approach is characterized
by Chizzotti (1991) and Gil (1996) as a tool composed by pre-elaborated and sequentially
placed questions with the aim to obtain answers relating to a specific subject. Marconi and
Lakatos (1996) add stating that this approach generates quick and precise answers, as well
as provide uniformity of information. The procedures carried out were similar to those
performed by Chia et al (2009).
Fourthly, data analysis was performed through descriptive statistics. According to
Levin (1987), descriptive statistics aims to gather data into groups in a way that allows
easy identification of their characteristics. Frequency distributions were applied to identify
sample distribution among supply chain roles as well as performance indicators use
patterns. Two value references were estimated in order to identify eligible performance
indicators for the Balanced Scorecard frameworks relating to input suppliers, producers,
distributers and retailers:
• The upper quartile value;
• The use patterns of 75% considering the estimated error.
These procedures should be able to generate specific Balanced Scorecard
frameworks for the supply chain roles considered, as well as to identify similarities and
differences among them.
4. Results
Initially, descriptive statistics was carried out to identify the frequency distribution
of individual companies from the sample among the four supply chain roles considered.
The results are presented in table 1.
Table 1: Frequency distribution of individual companies among supply chain roles
Specific roles Frequency
Input suppliers 31
Producers 13
Distributors 47
Retailers 30
These results reveal that there is a relative balance distribution of individual
companies into the four supply chain roles. The second step consisted in identifying the
performance indicators use patterns from the balanced scorecard perspectives. The results
relating to performance indicators from the financial perspective of the BSC are presented
in table 2.
Table 2: Performance indicator use patterns from the financial perspective considering
supply chain role (percentages)
Performance indicators
Input suppliers
Producers
Distributors
Retailers
Profitability 90.32 84.62 65.96 100.00
Liquidity 6.45 53.85 51.06 13.33
Revenues by products 32.26 61.54 48.94 20.00
Revenue per employee 3.23 23.08 17.02 0.00
Contribution margin 3.23 30.77 25.53 0.00
Level of indebtedness 3.23 23.08 36.17 40.00
Return over investment 16.13 15.38 19.15 20.00
Unit cost 67.74 61.54 38.30 3.33
Minimizing costs 70.97 84.62 59.57 100.00
Profit maximization 38.71 61.54 36.17 23.33
Inventory 3.23 61.54 12.77 3.33
Overall earnings 12.90 38.46 23.40 3.33
Operation costs 45.16 76.92 25.53 0.00
After calculating the use patterns of all financial performance indicators tested, the
reference values set was estimated. The results are presented in table 3.
Table 3: Quartile references values relating to financial performance indicators
Supply chain roles Quartile
Input suppliers
Producers
Distributors
Retailers
Upper quartile 56,45 69,23 50,00 31,66
Maximum 90,32 84,61 65,95 100,00
According to the results, some similarities as well as some particularities were
found. Financial performance indicators relating to profitability and minimizing costs were
present in upper quartile for all supply chain roles considered. These findings indicate that
they are taken in high level of importance for individual agribusiness companies,
regardless of their position in supply chain structure. On the other hand, each role also
indicated significant specific performance indicators use patterns relating to their
respective characteristics (unit costs among input suppliers, operational costs among
producers, liquidity among distributors and level of indebtedness among retailers).
The percentage error references relating to financial performance indicators were
estimated. The results are presented in table 4.
Table 4: Percentage error references relating to financial performance indicators
Supply chain roles References
Input suppliers
Producers
Distributors
Retailers
Percentage 75 75 75 75
Estimated errors 7 5 4 9
Reference percentage 68 70 71 66
Using the percentage references the number of suitable performance indicators was
smaller in comparison with the results presented in table 3. Profitability and minimizing
costs were suitable for input suppliers, producers and retailers. No individual performance
indicator could match the percentage among distributors. These results suggest that the
eligible group of performance indicators change significantly if different reference values
are applied.
The same procedures were carried out considering the performance indicators use
patterns from the customer perspective of the balanced scorecard. The results are presented
in table 5.
Table 5: Performance indicator use patterns from the customer perspective considering
supply chain role (percentages)
Performance indicators
Input suppliers
Producers
Distributors
Retailers
Customer satisfaction 87,10 84,65 72,34 76,27
New customers 80,65 61,54 34,04 43,33
Customer loyalty 51,61 61,54 63,83 46,67
Market share 32,26 61,54 42,55 0,00
Brand value 19,35 53,85 14,89 0,00
Profitability by customer 6,45 46,15 25,53 10,00
Revenue per customer 3,23 53,85 38,30 10,00
Business partners satisfaction 35,48 61,54 19,15 26,67
Delivery time 90,32 0,00 21,28 23,33
Responsiveness to clients 3,23 23,08 12,77 3,33
Growth in market share 3,23 38,46 12,77 0,00
Maximizing sales 35,48 76,92 42,55 63,33
Following the same procedures, reference values set. The results are presented in
table 6.
Table 6: Quartile references values relating to customer performance indicators
Supply chain roles Quartile
Input suppliers
Producers
Distributors
Retailers
Median 34,05 50,42 27,89 19,62
Upper quartile 35,48 61,53 38,29 26,66
Maximum 90,32 76,92 42,55 63,33
Considering the references percentages calculated, the results found were similar to
the results relating to financial performance indicators. Customer performance indicators
relating to customer satisfaction, customer loyalty and business partners satisfaction were
present in upper quartile for all supply chain roles considered. These findings corroborate
that different supply chain roles use some similar performance indicators. However,
particularities were also found, such as delivery time and market share.
The percentage error references relating to customer performance indicators were
estimated. The results are presented in table 7.
Table 7: Percentage error references relating to customer performance indicators
Supply chain roles References
Input suppliers
Producers
Distributors
Retailers
Percentage 75 75 75 75
Estimated errors 8 8 3 8
Reference percentage 67 67 72 67
Only the performance indicator relating to customer satisfaction was suitable for all
supply chain roles. New customers and delivery time was suitable for input suppliers and
maximizing sales was suitable for producers. These results obtained for customer
performance indicators corroborate the results for financial performance indicators,
presenting significant changes among performance indicators eligible group if different
reference values are applied.
The use patterns were also calculated for internal processes performance indicators.
The results are shown in table 8.
Table 8: Performance indicator use patterns from the internal processes perspective
considering supply chain role (percentages)
Performance indicators
Input suppliers
Producers
Distributors
Retailers
New products 87,10 53,85 40,43 86,67
New processes 35,48 76,92 29,79 33,33
Productivity by business unit 6,45 53,85 14,89 0,00
Products turnover 3,23 46,15 36,17 3,33
After sales 12,90 53,85 25,53 30,00
Operational cycle 51,61 53,85 14,89 0,00
Suppliers 54,84 46,15 46,81 26,67
Waste 3,23 61,54 42,55 13,33
Flexibility 12,90 69,23 34,04 3,33
Response time to customers 3,23 0,00 8,51 10,00
Delay in delivery 0,00 0,00 8,51 16,67
Response of suppliers 35,48 61,54 19,15 26,67
Storage time 6,45 53,85 34,04 3,33
Information and integration of materials 0,00 38,46 8,51 0,00
Once more, the reference values set were estimated. The results are presented in
table 9.
Table 9: Quartile references values relating to internal processes performance indicators
Supply chain roles Quartile
Input suppliers
Producers
Distributors
Retailers
Median 23,82 51,47 27,33 18,71
Upper quartile 35,48 61,53 36,17 26,66
Maximum 87,09 76,92 46,80 86,66
The results show the presence of some particularities. Operational cycle was
identified in input suppliers’ upper quartile, while flexibility, product turnover and after
sales were found, respectively, in producers, distributers and retailers. None of the internal
processes performance indicators tested was found in upper quartile for all supply chain
roles. These findings indicate that this perspective is particularly sensitive to specific
aspects of supply chain roles.
Adopting the same procedures, the percentage error references relating to internal
processes performance indicators were also estimated. The results are presented in table
10.
Table 10: Percentage error references relating to internal processes performance indicators
Supply chain roles References
Input suppliers
Producers
Distributors
Retailers
Percentage 75 75 75 75
Estimated errors 7 4 3 7
Reference percentage 68 71 72 68
None of the internal processes performance indicator tested was considered
suitable. New products performance indicator was eligible for both input suppliers and
retailers, and new process was eligible for producers. These findings corroborate the
assumption that different reference values affect the performance indicator’s composition
for specific supply chain roles.
At last, the same procedures were carried out considering the performance
indicators use patterns from the learning and growth perspective of the balanced scorecard.
The results are presented in table 11.
Table 11: Performance indicator use patterns of performance indicators from the learning
and growth perspective that presented null hypothesis rejections (percentages)
Performance indicators
Input suppliers
Producers
Distributors
Retailers
Investment in training 9,68 69,23 40,43 40,00
Investment in technology 6,45 69,23 55,32 13,33
Investment in information systems 12,90 69,23 40,43 16,67
Employee motivation 51,61 38,46 48,94 13,33
Employee capability 67,74 46,15 36,17 23,33
Managerial efficiency 6,45 53,85 27,66 6,67
Employee satisfaction 38,71 53,85 51,06 6,67
Innovation management 3,23 53,85 17,02 3,33
Number of complains 22,58 0,00 12,77 0,00
Risk management 0,00 38,46 14,89 0,00
Note: Kruskal-Wallis results (p=0,00)
Once again, the quartile reference values were estimated. The results are presented
in in table 12.
Table 12: Quartile references values relating to learning and growth performance
indicators
Supply chain roles Quartile
Input suppliers
Producers
Distributers
Retailers
Upper quartile 38,70 69,23 48,93 16,66
Maximum 67,74 69,23 55,31 40,00
Considering these references values, none of the learning and growth performance
indicators tested was found in upper quartiles of the four supply chain roles. These findings
corroborate the presence of specific aspects of supply chain roles relating to performance
indicators use patterns.
The percentage error references relating to learning and growth performance
indicators were estimated too. The results are presented in table 13.
Table 13: Percentage error references relating to internal processes performance indicators
Supply chain roles References
Input suppliers
Producers
Distributors
Retailers
Percentage 75 75 75 75
Estimated errors 7 7 5 3
Reference percentage 68 68 70 72
Only producers presented performance indicators that could match the reference
percentage (investment in training, investment in technology and investment in
information system). Once more, the results corroborate the different reference values
affect the performance indicator’s composition for specific supply chain roles.
After identifying the eligible performance indicators, the specific Balanced
Scorecard Frameworks relating to supply chain roles were formed. The Balanced
Scorecard Frameworks structures considering upper quartiles’ references values are
presented in table 14.
Table 14: Balanced Scorecard profiles according to supply chain role
Perspectives Input suppliers Producers Distributers Retailers
Financial
• Profitability
• Unit costs
• Minimizing
costs
• Profitability
• Minimizing
costs
• Operational
costs
• Profitability
• Liquidity
• Minimizing
costs
• Profitability
• Level of
indebtedness
• Minimizing
costs
Customer
• Customer
satisfaction
• New customers
• Customer
loyalty
• Business
partners
satisfaction
• Delivery time
• Maximizing
sales
• Customer
satisfaction
• New customers
• Customer
loyalty
• Market share
• Business
partners
satisfaction
• Maximizing
sales
• Customer
satisfaction
• Customer
loyalty
• Market share
• Revenue per
customer
• Business
partners
satisfaction
• Customer
satisfaction
• New customers
• Customer
loyalty
• Business
partners
satisfaction
• Maximizing
sales
Internal processes
• New products
• New processes
• Operational
cycle
• Suppliers
• Response of
suppliers
• New processes
• Waste
• Flexibility
• Response of
suppliers
• New products
• Products
turnover
• Suppliers
• Waste
• New products
• New processes
• After sales
• Suppliers
• Response of
suppliers
Learning and growth
• Employee
motivation
• Employee
capability
• Employee
satisfaction
• Investment in
training
• Investment in
technology
• Investment in
information
systems
• Investment in
technology
• Employee
motivation
• Employee
satisfaction
• Investment in
training
• Investment in
information
systems
• Employee
capability
The configuration of the Balanced Scorecard configurations relating to each supply
chain role show that they share some similarities regarding to management control
concerns, as well as the number or performance indicators included. But the presence of
some specific focus according to their role can be identified. Specific performance
indicators for each supply chain role considered can be found in three perspectives of the
balanced scorecard presented. Only the learning and growth perspective did not present
any specificity.
The Balanced Scorecard Frameworks structures considering the percentage
reference are presented in table 15.
Table 15: Balanced Scorecard profiles according to supply chain role
Perspectives Input suppliers Producers Distributers Retailers
Financial
• Profitability
• Minimizing
costs
• Profitability
• Minimizing
costs
• Operational
costs
• Profitability
• Minimizing
costs
Customer
• Customer
satisfaction
• New customers
• Delivery time
• Customer
satisfaction
• Maximizing
sales
• Customer
satisfaction
• Customer
satisfaction
Internal processes
• New products
• New processes
• New products
Learning and growth
• Investment in
training
• Investment in
technology
• Investment in
information
systems
The configuration of the specific balanced scorecard relating to each supply chain
role show that they share some similar concerns about management control, but also
possess specific focus according to their role. Three perspectives of the balanced scorecard
presented specific performance indicators for each supply chain role considered. Only the
learning and growth perspective did not present any specificity.
These results demonstrate that selection criteria adopted for the definition of
performance indicators groups for performance measurement among different supply chain
roles may affect directly the set of eligible performance indicators. Furthermore, the
identification of both common and specific performance metrics should be taken in
consideration too.
6. Conclusions
The objective of this article was to identify particularities of Balanced Scorecard
frameworks relating to specific supply chain roles. A sample composed by one hundred
and twenty-one individual Brazilian agribusiness companies was investigated through the
use of descriptive statistics.
The results presented statistically significant evidence that the balanced scorecard
profiles are not the same for all supply chain roles, although several common performance
indicators have been identified. The presence of particular performance indicators relating
to specific supply chain roles indicate that future attention should be addressed to this
phenomenon through further investigation.
These findings support the presence of use specificities among different roles of
supply chain regarding performance metrics as well as they point out that any
implementation of a Supply chain performance measurement system should consider the
use of both common and specific performance indicators.
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