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Benchmarking Agri-food Supply Chain Networks: A Conceptual Framework
Muhammad Moazzam, PhD Candidate, Institute of Food Nutrition and Human Health, Logistics
and Supply Chain Management, Tennant Drive, Massey University, Palmerston North, 4474, New
Zealand. Email: [email protected]
Elena Garnevska, Lecturer, Institute of Food Nutrition and Human Health, Agribusiness, Tennant
Drive, Massey University, Palmerston North, 4474, New Zealand.
Email: [email protected]
Norman E. Marr, Professor, Institute of Food Nutrition and Human Health, Logistics and Supply
Chain Management, Tennant Drive, Massey University, Palmerston North, 4474, New Zealand.
Email: [email protected]
Abstract
Benchmarking has emerged as an effective tool for measuring and improving performance.
Benchmarking of agri-food supply chain networks (SCNs) is complex due to their unique features
including perishability and shelf life, seasonality of production, variability of quality and quantity,
long production throughput time, and need of specialized transportation. This paper proposes to
benchmark performance and best practices of milk SCNs in Pakistan and New Zealand. A
conceptual framework based on Supply Chain Operations Reference (SCOR) model conforming to
the specific needs of agri-food SCNs is proposed to measure performance and identify best
practices. The framework integrates relevant food quality measures with the SCOR metrics.
Moreover, the framework involves performing Gap Analysis to identify performance gaps between
milk SCNs in Pakistan and New Zealand. The best practices leading to superior performance in
milk SCN in New Zealand will be recommended for milk SCN in Pakistan with appropriate
tailoring to the local situation.
Keywords: Benchmarking, agri-food supply chain network, performance measurement, best
practices, SCOR model, and Gap Analysis.
Corresponding Authors’ Biography
Muhammad Moazzam is Pakistani born, and has lived and worked in Pakistan before
coming to New Zealand in 2009. Muhammad completed his Master in Business Administration
(MBA) with marketing and agribusiness specialization in 2006 from University of Agriculture,
Faisalabad-Pakistan and joined the same university as lecturer in marketing and agribusiness in
2006. As a lecturer, Muhammad was involved in teaching, research, and as a member of the
university’s administration team. In 2009, he qualified for Pakistan’s most privileged scholarship
from Higher Education Commission (HEC) of Pakistan for MS leading to PhD at Massey
University, New Zealand. Currently, Muhammad is a second year PhD student exploring best
practices in agri-food supply chains to be helpful for the improvement of milk supply chain network
in Pakistan. Muhammad’s research interests include supply chain management, agribusiness
management, and benchmarking. He has published few papers/articles on agribusiness and supply
chain management. He has also delivered a seminar at Massey University, New Zealand.
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Benchmarking Agri-food Supply Chain Networks: A Conceptual Framework Muhammad Moazzam, Elena Garnevska, and Norman E. Marr
1) Background
In globalised market place businesses are increasingly operating as part of collaborative
networks, known as supply chains. Partners of a supply chain share information and skills to offer
superior value to their customers. Now supply chains compete instead of individual businesses. In
order to achieve comparative advantage and some time to surpass their competitors, organizations
have always been exploring best practices leading to superior performance. Benchmarking is one of
them and has emerged as the most effective tool for organizational improvement.
In recent years, international food markets have faced unusual price fluctuations (see Figure
1). These price fluctuations have seriously affected the world food security situation. In developing
countries, like Pakistan, the number of people with inadequate food consumption (less than 2,100
kcal/capita/day) increased from 72 million (45% of the total population) in 2005-06 to 84 million
(51%) in 2008 (FAO, 2008). Inherent issues of poor performing agriculture sector are the additional
factors ever posing serious threat to the national food security situation.
Figure 1: Evolution of FAO Food Price Indices
Adopted from: (FAO, 2012b)
2) What to benchmark?
Agriculture is an important sector in Pakistan economy, employing 45% of the total labour
force and contributing 21% to the national GDP (Government of Pakistan, 2012). The agriculture
sector is divided into major crops, minor crops, and livestock subsectors. The livestock subsector is
the major contributor to the overall agriculture value added (55.1%) and counts 11.6 percent to
national GDP (Government of Pakistan, 2012). The total milk production for the year 2011-12 was
47.95 million tons out of which only 38.69 million tons was available for human consumption and
the remaining 9.26 million tons (19.3%) was lost either during transportation or calving
(Government of Pakistan, 2012).
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Pakistan is the fifth largest milk producing country of the world (FAO, 2012a), with dairy as
one of the fastest growing industry. In spite of all this, Pakistan still imports infant milk powder of
value 134.3 US$ million annually (Government of Pakistan, 2012). Various authors (Afzal, 2008;
Tariq et al., 2008; Zia, 2009) highlight a number of issues in milk SCN in Pakistan responsible for
the overall inefficiencies and poor performance. These are: traditional production and marketing
channels, poor milk production practices, unorganized farming community, seasonal demand and
supply patterns, lack of access to financial services, monopolistic and exploitative role of
middlemen, poor infrastructure, price fixation and unsatisfactory role of government agencies. In
order to identify the poor performing areas, to gauge the extent of poor performance, and to search
out best practices helpful in improving the overall performance, a benchmarking study of milk
supply chain network (SCN) in Pakistan dairy industry and milk SCN in New Zealand dairy
industry is proposed.
New Zealand (NZ) is an open mixed economy with annual GDP 185 NZ$ billion and annual
exports around $11 billion (Statistics NZ, 2011). NZ dairy industry is the country’s biggest export
earner. NZ market consumes around 5% of production whereas the remaining 95% goes to over 150
countries of the world with key markets in China, US, Japan and the EU (Fonterra, 2010). With
only around 2% of world production, NZ is the world’s largest exporter (almost one third of global
market) of dairying ingredients (Fonterra, 2010).
The proposed study aims to identify the poor performing areas and to suggest appropriate
measures to improve its performance. The milk SCN in NZ dairy industry is selected as a best-in-
class benchmark for the research study. The objectives of the study are:
1. To investigate the milk SCNs in Pakistan and NZ dairy industries.
2. To measure and benchmark the performance of milk SCNs in Pakistan and NZ.
3. To identify the performance gap between both the milk SCNs.
4. To suggest measures to improve the performance of milk SCN in Pakistan.
3) Literature Review
Organizations benchmark for a variety of reasons including: for improving quality,
performance, and customer service ; for successful implementation of business process re-
engineering, total quality management, and best practices; for identification of performance gaps
and best practices; and for continuous improvement (Yasin, 2002). There is no one universal way to
conduct benchmarking (Camp, 1989). Andersen et al (1999) interpret benchmarking process in four
simple steps: measuring ones’ own and the benchmarking partners’ performance level; comparing
performance levels, processes, and practices; learning from the benchmarking partners’ best
practices; and improving ones’ own organization.
A number of benchmarking frameworks are available in the literature. To suggest an
appropriate methodology to measure and benchmark the performance of agri-food SCNs,
benchmarking frameworks are organized into business excellence models and benchmarking
frameworks developed by academicians, practitioners, and managers. Renowned business
excellence awards are given in table 1.
Table 1: Business Excellence Models
Business Excellence Models Administered By
Global Benchmarking Network Informationszentrum Benchmarking (IZB)
Germany
Process Classification Framework American Productivity and Quality Center, USA
Baldrige Criteria for Performance Excellence National Institute of Standards and Technology,
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USA
European Foundation for Quality Management
(EFQM) Excellence Model
European Foundation for Quality Management,
Europe
Singapore Quality Award (SQA) Framework SPRING Singapore, Singapore
Canadian Framework for Business Excellence National Quality Institute, Canada
Australian Business Excellence Framework Australian Quality Council, Australia
Source: (BPIR 2012a)
Business excellence models are fundamentally diagnostic in nature and focus on identifying,
developing, and promoting best practices leading to superior performance. The key performance
indicators used by business excellence awards target performance excellence for individual firms
and not for the networks of businesses such as supply chains which leads to the local optimization.
The conflict of local versus global optimization provides the basis for performance measurement in
supply chain management.
Zairi and Ahmed (1999) report that the literature on benchmarking has reached its maturity
and criticize that most, if not all, of the benchmarking methodologies preach the same basic rules.
The commonly used frameworks developed by academicians, practitioners, and individual
organizations are presented in Table 2. Balanced scorecard and SCOR model are commonly used
for PM in supply chains (Li et al., 2011; Tuominen et al., 2009).
Table 2: Benchmarking Frameworks
Benchmarking Frameworks Remarks
Xerox benchmarking methodology by
Robert Camp
A ten-step process is organized into five phases: planning,
analysis, integration, action, and maturity.
Balanced Scorecard by Kaplan and
Norton
A PMS that complements financial indicators with PMs
for customers, internal business processes, and innovation
and improvement activities.
Spendolini’s five-step benchmarking
process
These steps are: determine what to benchmark; form a
benchmarking team; identify benchmark partner; collect
and analyze benchmarking data; and take action.
Codling’s twelve-step benchmarking
process
Twelve steps are categorized into four operational stages:
planning, analysis, action, and review and recycle.
Business Performance Improvement
Resource (BPIR)
The BPIR improvement cycle is a nine-step
benchmarking process.
TRADE methodology by COER Ten step TRADE methodology stands for: Terms of
Reference, Research, Act, Deploy, and Evaluate.
Supply Chain Operations Reference
(SCOR) model by Supply Chain
Council (SCC)
A cross-industry reference model structured around five
processes: Plan, Source, Make, Deliver, and Return and
four levels of process details.
Sources: (Camp, 1989; Codling, 1992; COER, 2012; Kaplan & Norton, 1992; BPIR, 2012b;
Spendolini, 1992; SCC, 2012)
The frameworks specifically designed to benchmark supply chains, in general, and agri-food
supply chains, in particular, are very few. For example, Gilmour (1998) develops a framework to
benchmark supply chain operations. The framework comprises of 11 capabilities (6 process, 2
information technology, and 3 organizational) to evaluate the characteristics of a supply chain.
Prado (2001) benchmarks quality assurance system of Spanish companies from different sectors.
Garcia et al. (2004) develop a three dimensional benchmarking framework to assess quality
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performance gap in food standards of international supply chains. Tuominen et al. (2009) use
supply chain balanced scorecard to benchmark Russian and Finnish food industry supply chains.
Yakovleva et al. (2009) develop a framework for benchmarking sustainability of food supply chains
in UK. A voluntary group in NZ dairy industry develops a benchmarking system for dairy farmers
mainly focusing the KPI’s: cash (liquidity), profit, and wealth creation (Shadbolt, 2009).
Performance measurement is the key element in benchmarking process and the
appropriateness of PMS used determines the overall effectiveness of benchmarking practice. To
help selecting an appropriate PMS, the major issues in supply chain PM must be kept in mind.
Those commonly discussed in the literature are:
The use of too many matrices (Van Aken and Coleman, 2002).
The use of only short term focused matrices (Van Aken and Coleman, 2002).
Weak link between measures and strategy (Holmberg, 2000; Van Aken and Coleman, 2002).
A heavy reliance on financial and cost metrics (Beamon, 1999; De Toni and Tonchia, 2001;
Holmberg, 2000; Van Aken and Coleman, 2002).
Lack of balanced approach (Beamon, 1999; Chan, 2003).
A confusing multitude of isolated and incompatible measures (Holmberg, 2000; Van Aken
and Coleman, 2002).
Spanning single firm, not the entire supply chain (Beamon, 1999; Chan, 2003; Lambert and
Pohlen, 2001).
Insufficient focus on customers and competitors (Beamon, 1999).
The literature on supply chain performance measurement systems is too large and multi-
dimensional to develop a clear understanding from all aspects. However, the categorization of
performance measurement systems organized by Ramaa et al. (2009) is helpful. These categories
are: function based measurement system (FBMS), dimension based measurement system (DBMS),
supply chain operations reference model (SCOR), supply chain balanced scorecard (SCBS),
hierarchical based measurement system (HBMS), interface based measurement system (IBMS), and
perspective based measurement system (PBMS).
A. Function based measurement system (FBMS)
A FBMS is given by Christopher (1995) namely industry average cost model useful for
measuring performance of the individual functions performed in an organization. According to
Lapide (2000) the industry average cost model is diagnostic in nature and therefore is helpful in
identifying problem areas. Furthermore in FBMS each function is evaluated in isolation from the
supply chain which leads towards the local optimization at the cost of entire chains’ performance
(Lapide, 2000). Ramaa et al (2009) add that the approach is easy to implement and is suitable when
individual departments’ performance is needed to be optimized.
B. Dimension based measurement system (DBMS)
A substantial number of researchers view performance measurement from different
dimensions (Aramyan et al., 2006; Beamon, 1999; Chan, 2003; Gunasekaran et al., 2001; Neely et
al., 1995) as summarized in Appendix. The Appendix shows a trend that during 1990s’ mainly
quantitative measures of performance were employed by the researchers to value their businesses
whereas the use of qualitative measures in combination with quantitative measures predominant in
subsequent years.
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C. Supply chain operations reference (SCOR) model
The Supply Chain Council (SCC) of USA introduces its first Supply Chain Operations
Reference (SCOR) in 1997 (Stewart, 1997). The model is structured around five processes: Plan,
Source, Make, Deliver, and Return and four levels of process details (Supply Chain Council, 2012).
Stewart (1997) view SCOR model as the first cross-industry reference model and recommends for
operational improvement in an organization. The SCOR model provides strategic visibility to the
performance of entire supply chain (Lapide, 2000). Simatupang and Sridharan (2004) view SCOR
model to be the most suitable for benchmarking for its comprehensiveness and standard process and
metrics definitions which enable companies to evaluate and improve performance at individual as
well as entire supply chain levels. Aramyan et al (2006) admire the holistic nature of SCOR model.
A few weaknesses have also been identified in SCOR model. For example, one of the SCOR
processes “Return” does not apply on services business (Ellram et al., 2004). According to
Aramyan et al (2006) criticise SCOR model for insufficient focus on food quality measures (a
distinguishing characteristic of agri-food SCs), sales and marketing, R & D, product development,
and after-sale customer service. They go on to say that it assumes but not sufficiently addresses:
quality, information technology, training, and administration. Furthermore, Burgess and Singh
(2006) add that SCOR model does not address to the complex social and political factors which are
integral part of certain supply chains.
D. Supply chain balanced scorecard (SCBS)
Kaplan and Norton (1992) devise balanced scorecard (BSC) to help managers from which to
choose measures. Balanced scorecard complements traditional financial indicators with
performance measures for customers, internal business processes, and innovation and improvement
activities. Lapide (2000) acknowledges the idea of alignment of executive level measures with
strategic objectives. Moreover, BSC allows companies to identify niche markets through external
customer involvement in strategy development (Bourne et al., 2003). There are some limitations of
BSC are also found. According to Neely et al (1995) BSC provides inadequate assistance for the
process of designing a performance measurement system and competitive benchmarking. More
importantly it does not provide a holistic view spanning entire supply chain rather it captures the
performance of individual organization (Lambert and Pohlen, 2001). Moreover, BSC focuses only
executive enterprise level (tactical and strategic level) measures and not the management level
(operational level) measures (Aramyan et al., 2006; Lapide, 2000).
E. Hierarchical based measurement system (HBMS)
The HBMS is given by Gunasekaran et al (2001). The framework classifies the performance
measures into hierarchical levels of management: strategic, tactical, and operational and dimensions
as financial and non-financial so that a suitable costing method based on activity analysis can be
applied. Furthermore, these metrics are aligned into four processes: plan, source, make, and deliver
that mainly constitute a supply chain. Bhagwat and Sharma (Bhagwat and Sharma, 2007) believe
that HBMS is helpful in selecting the appropriate metrics and costing methods at different levels in
an organization.
F. Interface based measurement system (IBMS)
Lambert and Pohlen (2001) devise a framework to align the performance of each link within
the supply chain. The link-by-link approach looks at the supply chain as a series of different
interfaces and aims to optimise the performance at individual links level as well as the supply chain
as a whole. The IBMS is appreciated by the research for a variety of reasons. The interfaces can be
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used to demonstrate the outcome of supply chain collaboration (Pohlen, 2003). It focuses on
managing customer relationships and supplier relationships at each link in the supply chain
(Gaiardelli et al., 2007). Moreover, IBMS is helpful in assessing the benefits of the collaborative
efforts in supply chain management (Mentzer et al., 2007).
G. Perspective based measurement system (PBMS)
Otto and Kotzab (2003) develop a framework to measure supply chain performance from all
the six possible perspectives: system dynamics, operations research, logistics, marketing,
organization, and strategy. The PBMS can be employed to identify problems, their possible
solutions, and to optimize the trade-off of measures among the perspectives (Hofmann, 2006).
However, the existence of different perspectives makes it difficult to identify the significance level
of different areas of performance measurement in a supply chain (Papakiriakopoulos and Pramatari,
2010).
This review offers a critic of existing performance measurement and benchmarking
frameworks. Having known the advantages and disadvantages of the PMSs, it is learned that the
selection of an appropriate PMS is case-specific. Moreover, the purpose of research also affects the
selection of a set performance measures. The above review suggests that the SCOR model is
comparatively more holistic and comprehensive in measuring performance of complex supply
chains. However, appropriate modifications conforming to the specific needs of agri-food supply
chain is needed.
4) Methodology
The choice of right PMS for a supply chain very much depends upon the nature of problem(s)
that the research team is going to address. The SCOR model suggested by the literature review is
the framework that links performance metrics, supply chain processes, best practices, and people
into a unified structure. The model is constructed on five supply chain processes: Plan, Source,
Make, Deliver, and Return (see Figure 2).
Figure 2: SCOR-Model’s Processes
Source: (SCC, 2012)
To measure performance of five supply chain processes the SCOR model identifies five
supply chain performance attributes: reliability, responsiveness, agility, costs, and assets
management (see Figure 3). Reliability, responsiveness, and agility are customer focused whereas
cost and asset management are a supply chain’s internal focused performance attributes. To modify
SCOR model conforming to performance measurement in agri-food supply chains, the relevant
food quality attributes are added as level-3 metrics to the SCOR metrics.
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Figure 3: Conceptual Framework for Performance Measurement in Agri-Food SCs
Source: Adopted from (SCC, 2012)
Food quality
Food quality implies product quality and process quality (Aramyan et al., 2006). A number of
food quality metrics are found in the literature, however, the most relevant ones are given in the
Table 3. The selected metrics (given in table 3) are incorporated in the relevant performance
attribute and at appropriate level of SCOR metrics.
Table 3: Proposed Performance Attributes for Food Quality
FQ.1.1 Product Quality FQ.1.2 Process Quality
FQ.2.1 Sensory properties and shelf-life FQ.2.2 Production system
FQ.2.3 Food safety FQ.2.4 Product handling and transportation
FQ.2.5 Food nutrition FQ.2.6 Environmental aspects
FQ.2.7 Packaging
Source: Adopted from (Aramyan et al., 2006; Hooker and Caswell, 1996).
5) Future Research
This paper introduced a research problem of low performing milk systems in Pakistan and
reviewed the existing frameworks to measure and benchmark the performance in supply chain
management, in general, and agri-food supply chains, in particular. The literature review probed
into the pros and cons of existing frameworks and assessed their conformity to the proposed
research problem. A conceptual framework based on Supply Chain Operations Reference (SCOR)
model and conforming to the specific needs of agri-food supply chains has been proposed and needs
to be tested empirically. The proposed model will be pilot tested with the participants in milk SCNs
of Pakistan and New Zealand, before final data collection. The methods of personal interviews and
postal survey will be employed to measure performance and identify the best practices leading to
superior performance. Gap analysis will be performed to identify performance gaps between both
the milk SCNs. Best Practices from the Benchmark milk SCN (New Zealand) will be recommended
with sufficient tailoring to the local situation in Pakistan.
Food Quality
SCOR model
Reliability
Responsiveness
Agility
Assets
Internal-focused
performance attributes
Costs
Customer-focused
performance attributes
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Appendix
Dimension Based Performance Measurement Frameworks
Author(s) Performance Measurement Framework/System Dimensions
Neely et al. (1995) Quality, time, flexibility, and cost. QN, Q, F, C, T
Beamon (1999) Resource, output, and flexibility. QN, F
Li and O΄Brien
(1999)
Profit, lead time, delivery flexibility, and waste
elimination.
QN, F, T
Holmberg (2000) A systems’ perspective, cost, responsiveness. QN, C, R
De Toni and Tonchia
(2001)
Cost and non-cost. C, NF
Gunasekaran et al.
(2001)
Strategic, tactical, and operational focus. QN, QL, FN,
NF
12
Najmi and Kehoe
(2001)
Quality, time, and financial. QN, QL, Q,
FN, T
Chan (2003) Quantitative: cost, resource utilization. Qualitative:
quality, flexibility, visibility, trust, and innovativeness.
QN, QL, C, Q,
F, I
Suhnag Li et al.
(2005)
Strategic supplier partnership, Customer relationship,
information sharing, information quality, internal lean
practices, and postponement.
QN, QL, Q, C
Aramyan et al. (2006) Efficiency, flexibility, responsiveness, and quality. QN, F, R, Q
Li et al. (2011) SCOR model: reliability, responsiveness, agility, cost,
and asset.
QN, QL, R, A,
C
NB: The abbreviations used for the measurement dimensions are: agility (A), quantitative (QN),
qualitative (QL), quality (Q), cost (C), flexibility (F), responsiveness (R), financial (FN), non-
financial (NF), time (T), and innovativeness (I).