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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)


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


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


Input suppliers

Producers

Distributors

Retailers




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)


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


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


Input suppliers

Producers

Distributors

Retailers




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)


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


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


Input suppliers

Producers

Distributors

Retailers




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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