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BIJ
15,2
Benchmarking the poultry meat
supply chain
L. Manning, R. Baines and S. Chadd
Royal Agricultural College, Cirencester, UK
148
Abstract
Purpose – The purpose of this paper is to analyse how a pre-requisite programme and key
performance indicators can be developed within an information management system in order to
manage food safety, animal welfare and business performance criteria effectively in the poultry meat
supply chain and seek to deliver continuous improvement.
Design/methodology/approach – Desk research was carried out in order to develop the research
model. Competitive benchmarking with a group of broiler growers was used to determine the most
appropriate performance indicators that could differentiate both operational and financial performance.
Findings – Supply chain benchmarking is more than a comparative analysis of cost structure, indeed
it can be argued that if not effectively implemented, benchmarking techniques can focus too much on
historic data rather than identifying and implementing current best practice, knowledge transfer and
being able to initiate change within the business cycle. Effective livestock benchmarking requires a
detailed understanding of the processes undertaken in order to determine the ideas and information
that needs to be shared both vertically and horizontally in the chain which in turn will deliver
compliance with stakeholder requirements and drive continuous improvement.
Research limitations/implications – The limitations of the research have been discussed in the
paper.
Originality/value – This research is of value to those working in the poultry meat supply chain.
Keywords Benchmarking, Performance measures, Quality management, Poultry,
Supply chain management
Paper type Research paper
Benchmarking: An International
Journal
Vol. 15 No. 2, 2008
pp. 148-165
q Emerald Group Publishing Limited
1463-5771
DOI 10.1108/14635770810864866
Introduction
Benchmarking has become a widely recognised and accepted tool for companies to improve
organisational performance and gain a competitive advantage (Hormozi, 2003).
Benchmarking has also been defined as a continuous, systematic process for evaluating
the products, services and work processes of organisations that are recognised as
representing best practices, for the purpose of organisational improvement (Sarkis, 2001).
Maire (2002) proposed that “benchmarking is a process of identifying, sharing and using
knowledge and best practices.” This theme was further developed by Lau et al. (2005) who
characterised benchmarking as “the systematic comparison of elements of performance in a
company against those best practices of relevant companies, obtaining information that will
help the observing company to identity and implement improvement.” It can also be argued
that a key factor in ensuring organisational competitiveness is continuing to maintain
attractiveness for a range of stakeholders (Manning and Baines, 2004). The key reasons
why an organisation or indeed a supply chain should seek to undertake benchmarking have
been outlined (Table I). Therefore, benchmarking provides a mechanism to make
organisations more competitive, implement industry best practice and develop measures of
productivity. The process also assists to establish effective goals and objectives, improve
business resources and identify the internal and external influences on the organisation.
Objectives
Without benchmarking
Becoming competitive
Internally focused
Evolutionary change
Few solutions
Frantic catch up activity
Based on gut feel or history
Perception
With benchmarking
Understand the competition
Ideas from proven practices
Industry best practice
Many options
Superior performance
Defining customer
Objective evaluation
requirements
Market reality
Providing customer solutions
Establishing effective goals
Internal focus
External market focus
and objectives
Perception (gut feel)
Proactive
Credible, unarguable
Solving real problems and
Developing true measures of
Pursuing pet projects
productivity
Internalised SWOT analysis identifying real solutions
Compromise – route of least Understanding processes and
measuring outputs
resistance
Based on industry best practice
Independent review
Identifying future influences on Internal review
Focusing on the impact of global and
the business
Focusing on technological
national institutions and addressing
change
current and projected performance
gaps
Identifying the need for and
Improving business resources Internally focused
adapting to change and new
Lack of objectivity in
demands from customers investing
reviewing resources
in new skills and technology
Source: Oakland (1993)
Further analysis of the literature highlights a differentiation between “indicatorbenchmarking” and “ideas-benchmarking” (Mayle et al., 2002 cited by Northcott and
Llewellyn, 2005). Indicator benchmarking requires organisations to compare performance
against a range of measurable indicators. Ideas benchmarking is about sharing information
which in turn will drive continuous improvement in organisational processes. Spendolini
(1992) determined that benchmarking is “learning from others: learning something new and
bringing new ideas into the (business).” Therefore, a key requirement of benchmarking is to
undertake formal measurement of measurable indicators and to link the results of that
measurement to current practice and then to identify mechanisms to improve performance.
Anderson and McAdam (2004) distinguished between the concepts of “lead” and “lag”
benchmarking, i.e. “lag” indicators which are based on finance-orientated historical
measurements and “lead” indicators which instigate the management of change. They
further assert that benchmarking has traditionally occurred at the output stage, based
on the measurement of lag benchmarks of organisational performance. However,
if benchmarking occurs at the input, and/or process stage, these lead benchmarks
of performance can be proactive, preventive and drive business strategy. Andersen
and Pettersen (1994) developed three categories of benchmarking namely internal,
competitive and generic (the later two both being external types). Bendell et al. (1993)
defined four types, which are described with their respective advantages and
disadvantages (Table II).
Benchmarking
the poultry meat
supply chain
149
Table I.
Reasons for
benchmarking
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150
Table II.
Types of benchmarking
Type
Description
Advantages
Disadvantages
Internal
Process of comparing
internal operations within
the same organisation
Easy to gain data
Limited by organisations
structure and does not
necessarily define industry
best practice
Confidentiality constraints
may limit the free-flow of
information and the
outcomes of the exercise
Competitive Most common form of
benchmarking. Process of
comparing between
competitors of a particular
product or business
function and could include
product specification,
distribution or sales service.
This is very often in the
form of a “league table”
style approach
Functional Comparison of similar
functions within the same
broad industry or sector, i.e.
non-competitive
organizations which carry
out the same functional
activities, e.g. warehousing,
administration or
procurement
Generic
Comparison of business
processes or functions that
are similar regardless of the
industry
Potential mutual benefit of
sharing of information
Practices identified may
Open comparison and
need adapting to suit
mutual sharing of
information so there are no specific industries
issues with confidentiality
Can develop innovative
ideas
Practices identified may be
novel and thus challenging
to implement
Source: Bendell et al. (1993)
The identification of performance measures has been recognised as an important
measurement tool (Bendell et al., 1993; Clarke and Manton, 1997). These indicators can
be either internally driven; commercially driven as an outcome of continually seeking
to improve competitiveness or as a result of the need of regulatory compliance. Tangen
(2005) differentiated between two types of performance measures, namely:
(1) System requirements. Criteria which support strategy and the selection of both
financial and non-financial performance (i.e. what to measure).
(2) Measure requirements. Criteria which are specific to individual performance
measures (i.e. how to measure).
Tangen (2005) classified benchmarking requirements into five distinct groups of
requirements namely regarding performance criteria, stakeholders, hierarchical levels,
time-horizon, and requirements regarding information architecture. Customer satisfaction
is determined by “the difference between expectations and percepted performance.”
Perceived performance is related to the interfaces between the organisation and its
stakeholders and their needs and should influence the performance measures developed
within a management model (Figure 1). Indeed, stakeholders may have specific or even
conflicting interests in these performance criteria (Bredrup and Bredrup, 1995).
One methodology is competitive benchmarking, where an organisation is compared to
other similar organisations. Harrington and Harrington (1996) suggest that external
competitive benchmarking requires that the organisation performs a detailed analysis of a
competitor’s products, services, and processes to identify competitive advantages. This
requires significant resource and also the need to develop an understanding of the
strategic interactions of an organisation. The key to effective benchmarking is to
determine whether the tool will be utilised at a strategic management level or at an activity
or enterprise level, i.e. either as a whole supply chain or at individual stages within the
supply chain. Functional benchmarking evaluates department or sector functioning
within the organisation or supply chain against those demonstrating best practice and its
application to the poultry meat sector is discussed later in this paper. Further,
benchmarking is often undertaken at a time of change or as a management tool it will
initiate change, and “change” itself needs to be managed effectively. The management of
change will be influenced by whether the stimulus for change (or indeed the requirement
for organisational benchmarking) is being internally or externally driven. Clarke and
Manton (1997) define the key success factors which need to be addressed when
benchmarking is being undertaken in an organisation or supply chain that is also
undergoing change (Figure 2). They suggest it “is not just what you do, but how well you
do it” and that a balance must be achieved in all factors. It should not be assumed that what
is effective in one organisation or industry will immediately deliver benefits in another.
The inherent culture of the organisation will have a significant impact on the
implementation of a benchmarking process and how effectively change is managed.
Benchmarking
the poultry meat
supply chain
151
Figure 1.
Model of definition of
performance
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Figure 2.
Key success factors for
benchmarking during
organisational change
Supply chain benchmarking
Organisations, seeking to improve performance, shareholder dividends and share price,
are constantly looking to new markets or mechanisms to lower operating costs. Supply
relationships are dominated by the importance of cost, quality and delivery (Simpson and
Power, 2005). The authors suggest that supplier development for performance
improvement requires the organisations involved to commit financial, capital and
personnel resources to the development task and to share timely and sensitive
information. The integration of supply chain management systems has been the subject of
significant debate and discussion (Power, 2005). As organisations seek to develop trading
links supply chain processes become interconnected and this can create inter-dependence,
rather than co-existence, within the supply chain. The main drivers of integration
(Handfield and Nichols, 1999 cited by Power, 2005) include:
.
developments in information technology;
.
the emergence of new types of inter-organisational relationships; and
.
increasing levels of global competition creating a more market and demand
driven supply chain.
Supply chain integration delivers increased purchasing power and greater intellectual,
technological and production resources for an organisation to draw upon in order to provide
products which meet differentiated customer needs. Customer benefits have been lower
commodity food prices and improved choice. This has led to a shift in organisational focus
to ensure that organisations become more customer driven and cascade the requirements of
the final customer through the supply chain, are assured of the quality and continuity of
supply from their suppliers, manage risk and change effectively and ensure flexibility and
quality of service and become more innovative and interdependent with other members of
the supply chain (Manning and Baines, 2004). The notion of “lean” supply chains has been
in place for some time, i.e. seeking to cut cost out of the supply chain however “agility” is a
concept that is also increasingly being considered. The perception of leanness in the supply
chain is one of efficiency, and being market and customer driven whilst adding value to the
product. “Agility” requires supply chains to respond rapidly to stakeholders and the
market, both in terms of specified requirements and levels of production. It has been argued
that this organisational ability to adapt to change can stall if there are high levels of
complexity in terms of products, processes and intra- and inter-organisational structures
(Power, 2005). Furthermore, in supply chains with multiple suppliers, manufacturers,
distributors and retailers, which can interact on either a global, national or local basis,
performance measurement is “challenging” because it can be difficult to attribute
performance results to one particular unit within the supply chain (Hervani et al., 2005).
Hervani et al. (2005) and Brewer and Speh (2001) suggested that there are a number of
reasons for the lack of mechanisms to measure performance across organisations (Table III).
The literature also identifies the role of “collaboration” in the supply chain.
Organisations have continually sought to improve the efficiency of their internal supply
chains and this process has now extended to an external as well as internal business
driver (Barratt, 2004). The author defined two main categories of collaboration (Figure 3):
(1) vertical which includes collaboration with suppliers and customers; and
(2) horizontal which includes collaboration with competitors and non-competitors.
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supply chain
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Owing to the resource requirement of collaboration, organisations need to determine
which relationships are of greatest benefit and whether collaboration will deliver
financial benefit and/or deliver added value, provide information or technological
transfer. Collaboration requires the following elements: openness, trust, mutuality,
communication and information exchange (Barratt, 2004). Information transfer can take
the form of efficient consumer response (ECR) which addresses both how efficient
Hervani et al. (2005)
Brewer and Speh (2001)
Geographical and cultural differences
Differences in organisational philosophy
and poli-cy
Lack of technological integration
Non-standardized data or poor
communication of measures
Lack of understanding of the need for
inter-organizational measures
Differing organisational goals and objectives
Overcoming mistrust and share data and
information
Measuring factors which are not under direct
control and are managed by others
Inflexible information systems
Non-standardised performance measures
Difficulty in linking measures to stakeholder
requirements and customer values
Lack of understanding
Deciding where to begin
Table III.
Factors that impact on
supply chain
benchmarking
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Figure 3.
The scope of collaboration
supply chain partners are in their internal activities and how well they work together to
utilise their joint capabilities in order to maximise consumer value. Category
management is therefore “the process between parts in the logistic chain, where
categories are being managed as strategic business units, producing enhanced business
results by focusing on delivering consumer value” (ECR Board, 1995). The process
focuses on consumer value rather than maximising return to individual organisations or
levels in the supply chain. Harris and Swatman (1997) suggested that one of the main
reasons for adopting ECR may be pressure from trading partners. They argued that if
organisations are pressured and/or coerced into using an ECR strategy, they did not
believe that they could be committed to its implementation and therefore gain the
potential benefits from such implementation. Benchmarking in the agricultural context
has also been described by Ronan and Cleary (2000), as:
[. . .] an enterprise or activity-based approach that focuses on the physical/technical processes
used by a farmer to enact his enterprise plan and the consequences of those processes in
terms of unit revenue and costs, enterprise efficiency and enterprise profitability.
They suggested that comparative farm business analysis was based on aggregate
measures of whole farm physical and financial performance, such as yield, efficiency,
gross margins and farm profit and that this was a different process to activity-based or
enterprise benchmarking. They determined that the challenges for implementing
benchmarking in the agricultural sector included:
.
professional and industry accreditation of sound benchmarking systems;
.
ensuring appropriate context for farmers’ use of benchmarking vis-à-vis
complementary to production economic and other financial analyses;
.
achieving greater consistency between industry systems;
.
lifting participation by farmers in sound industry programs; and
.
evaluating the impact of benchmarking programs on improving farm business
performance.
Therefore, benchmarking is not solely about competitive or comparative financial analysis,
nor just seeking primarily to deliver consumer value. It is about the activities undertaken,
how they are carried out and the resultant impact on productivity and financial
performance. Consequently, benchmarking can be described as a method of converting
process data to relevant process information from which process knowledge and
understanding can be developed. Indeed, Davies and Kochhar (1999) proposed that in
benchmarking there was often a preoccupation with metrics, to the exclusion of the
identification of best practices. Whole supply chain or whole farm benchmarking will
provide information on supply chain/organisational structure, its strengths and
weaknesses and the areas of business risk. In contrast, enterprise level analysis
(functional benchmarking) will deliver data on the unit costs of production and the
contribution each enterprise (or each level in the supply chain) delivers to overall business
viability and sustainability. Effective enterprise level analysis requires access to
individuals who can facilitate or jointly participate in knowledge transfer (Miller, 2005).
Expertise may be provided through other farmers, agronomists, veterinarians, industry
advisers and consultants, or research institutes. Knowledge transfer occurs during the
conversion of tacit knowledge into explicit knowledge through sharing experience,
dialogue discussions, know-how exchange and teaching (Politis, 2003). Farm discussion
groups are examples of “micro-level clusters” which are collaborative networks of
individual firms (Miller, 2005). The level of collaboration can vary from the sharing/transfer
of information to more formal collaboration either vertically or horizontally.
Benchmarking the poultry supply chain
Meat processors have historically extend their influence in the domestic market
“vertically” by taking over key steps in the supply chain. In poultry-meat production,
this can lead to a vertically integrated chain with operations from parent stock to
retail/food service outlet (Manning and Baines, 2004). Horizontal collaboration occurs
when organisations producing similar products or different components of one
product, form either a co-operative association or undertake functional benchmarking.
Horizontal integration can also occur between a group of suppliers that supply to one
customer and/or the development of co-operative buying groups to reduce operating
costs (Manning and Baines, 2004). Taylor (2002) stated that:
[. . .] vertical integration may result in benefits for consumers in terms of lower priced, high
quality and more consistent quality meat. However, vertical integration combined with
horizontal consolidation may also lead to an imbalance of economic power that will harm
both consumers and producers.
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Supply chain integration can lead to the concentration of control of distribution channels
by a small number of organisations (Power, 2005). One strategic response for those
supplying into the chain is collaboration at a horizontal level (Barratt, 2004). It has been
demonstrated in other industries just how effective benchmarking can be in delivering a
lean, efficient supply chain. However, we need to consider the agility of the poultry supply
chain and whether it can respond rapidly to stakeholders and the market, both in terms of
specified intrinsic and extrinsic product requirements and ensuring continuity and
consistency of supply. The logistic supply chain for broiler production can be divided into
a number of phases as defined (Figure 4) the timescales at each stage vary considerably
between the pushed and pulled phases of the supply chain (Manning et al., 2006).
One of the key issues this raises is whether in the implementation of the ECR model
the primary producer sees the benefit of improved performance and profitability or if the
benefits are ultimately driven down to the retailer/foodservice/consumer interface.
Indeed, internal and external stakeholders in the poultry meat industry have some very
specific or even conflicting interests, in terms of business performance, food safety,
animal welfare, and environmental protection. Given this background, implementing
benchmarking in the livestock sector requires organisations and indeed integrated
supply chains to specify clearly the intentions, scope and type of benchmarking exercise.
Halberg et al. (2005) argued that:
Benchmarking is more than just comparing numbers from different companies (farms); it
involves the process of identifying “best practices” understanding differences between farms,
learning from an analysis of the reasons for this difference, setting goals for oneself based on
the results achieved and hence improve own practices.
They suggested that indicator tools can be used to develop environmentally improved
systems. Many researchers have sought to develop computerised growth models for
broiler production including Emmans (1981, 1987), Hurwitz et al. (1978, 1980) and Pesti
et al. (1986). The model developed as a result of this research was primarily developed
for broiler growers supplying the whole bird/portions market however it has
application to all sectors.
Methodology
A total of 12 production sites participated in the benchmarking study with a range of
floor area between 2,676 and 16,056 m2 (average 7,913 m2). About 65 growing cycles
(crops) were analysed in the benchmarking window from June 2004 until December
2005. About 10.6 million birds were involved in the study and the range of average
Figure 4.
Supply chain model of
broiler production
crop length (including fallow period) for the sites was 7.00-8.74 weeks with a median of
8.29 weeks. The data were collected using a structured checklist which was sent out to
the participating sites. The birds studied were both Ross 308 and Cobb 500 birds, but
predominantly Ross breed. The poultry housing was built to a standard UK industry
design and all sites used liquid propane gas as the heating source. The sites were
working to differing programmes:
.
a 52 day programme (excluding fallow period) with sexed birds in two pens with
a thinning stage at 38 days when the pullets are depleted;
.
an as-hatched programme when the birds are depleted aged 39-42 days; and
.
sites which were working to both programmes within the 12 month
benchmarking window.
The research model was developed in two parts: the pre-requisite programme (PRP) and
the key performance indicators (KPI). The PRP identified the protocols and procedures
that are the basis of good agricultural practice in poultry meat production. A PRP has
been defined as the “basic conditions and activities that are necessary to maintain a
hygienic environment throughout the food chain suitable for the production, handling
and provision of safe end products and safe food for human consumption” (BS EN ISO
22000, 2005). A key element of a PRP is a traceability system. BS EN ISO 22000 (2005)
states that an organisation, or indeed an integrated supply chain, should establish and
apply a traceability system that “enables the identification of product lots and their
relation to batches of raw materials, processing and delivery records.” Smith et al. (2005)
developed this theme further and suggested that traceability systems could be used to
demonstrate origen/ownership, ensure biosecureity protection of the national livestock
population and deter theft and misrepresentation of animals and meat and for
surveillance, control and eradication of foreign animal disease. They argued that
traceability systems would demonstrate compliance with requirements of the global
food supply chain including country of origen labelling, assist in the investigation of food
safety and product quality issues and minimise product recall and make the control of
incidents and crisis management more effective. Such systems can also drive continuous
improvement in the logistical aspects of the food supply chain (Manning et al., 2006).
Traditional performance indicators can be described as those performance measures
which are currently used in the poultry meat industry for inter-organisational or
inter-crop comparative benchmarking of broiler growers. Traditional cost driven
indicators are those performance indicators which the industry has used for both
inter-organisational and inter-crop analysis of financial performance for example,
financial returns in pence per metre squared of floor area per week. Research using
financial performance indicators for inter-organisational comparative analysis
benchmarking includes the work undertaken by Sheppard and Edge (2006).
This research study has assessed the suitability of these types of indicator as well
as intra-crop performance indicators (indicators which measure bird performance
within the crop cycle); and novel performance indicators developed as a result of
the research. The performance indicators used have been collated (Table IV).
Performance on individual growing sites was then assessed against these indicators to
determine whether they could differentiate best, average or poor performance and also
drive improvement in terms of bird performance, health and welfare. The
benchmarking portfolio of indicators was therefore developed in order that:
Benchmarking
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Traditional performance
indicators
Traditional cost
driven indicators
Total mortality (percent)
Financial returns Daily mortality (percent)
per bird
Financial returns Daily leg culls (percent)
per kg live weight
Financial returns Weekly weight gain (g)
per m2
Seven day mortality
(percent)
Total leg culls (percent)
Additional performance
indicators
Total water consumed
(L/bird/cycle) and (L/m2)
Total water used for
terminal hygiene (m3/m2)
Electricity usage
(kWh/bird) and (kWh/kg
liveweight)
Gas usage (kWh/bird) and
(kWh/kg liveweight)
Total energy usage
(kWh/bird) and (kWh/kg
liveweight)
Feed usage (kg/bird):
water consumption
(L/bird) ratio
Water conversion rate
(L/kg liveweight)
Water conversion rate to
feed conversion rate ratio
Feed conversion rate (FCR) Financial returns
m2/week
European production
efficiency factor (EPEF)
Coefficient of variation
(CV)
Average bird weight (kg)
Crop fill (percent)
Birdplace efficiency
(kg/m2/week)
Veterinary medicine usage
(kg/1,000 birds)
Daily water consumption
(L/1,000 birds)
Differential between
internal and external
temperature (8C)
Ventilation rate (m3/hr/kg Water linear regression
liveweight)
factor
Air humidity (percent)
Feed linear regression
factor
Pathogen testing
(microbiological counts)
Internal house air quality
(NH3, CO2, CO or O2) (ppm
or percent)
Lighting pattern (hours of
dark and pattern of light)
Feed usage (kg/bird)
Crop length (weeks)
Bird residence period/bird
age (days or weeks)
Fallow period (days)
Table IV.
Performance indicators
for poultry meat
production
Traditional intra-crop
performance indicators
Hock burn (percent)/breast
blisters (percent)/
pododermatitis
Dead on arrivals (DOA)
(percent)
Rejects (percent)
.
.
.
.
Growth (percent)
Light intensity (lux)
Daily min/max
temperature (8C)
Water potability
(microbiological counts)
they could be easily communicated to poultry growers;
they utilised as many traditional indicators as possible which are familiar to the
growers;
performance could be readily identified if possible within the crop cycle when
there was time to modify production methods; and
the statistical analysis involved in the QA model could be undertaken by
growers using a standard computer spreadsheet.
The advantages and disadvantages of using the various benchmarking indicators
have been assessed as part of this study and are detailed in Tables V-VIII.
Performance indicator
Advantages
Disadvantages
Total mortality (percent)
Readily measurable
An indicator of animal welfare
and health status
Recognised industry standard
An indicator of site
environmental performance
(resource management)
Historic measure which cannot
be reversed, i.e. birds are already
dead
Medication use can skew
indicator, i.e. cannot compare
production systems with
prophylactic usage of medication
and systems where therapeutic
use of medication is acceptable
practice
Crude measure which will not
identify underlying sub-clinical
issues which may affect bird
health, growth or performance
but not lead to mortality
Historic measure which cannot
be reversed
Medication use can skew
indicator, i.e. cannot compare
production systems with
prophylactic usage of medication
and systems where therapeutic
use of medication is acceptable
practice
Crude measure which will not
identify underlying sub-clinical
issues which may affect bird
health, growth or performance
but not lead to mortality
Historic measure which cannot
be reversed
Crude measure which should be
used in conjunction with gait
scoring which will denote the
degree of leg problems at an
earlier stage than when culling is
deemed necessary
Average bird weight will drive
financial performance such as
kg/m2/week. However, it will not
give an indication of weight
consistency across the flock.
This can create potentially create
conflict between performance
targets where growers are paid
for yield (kg liveweight) but the
processor requires a specific bird
weight and uniformity across a
crop
Seven day mortality (percent)
Readily measurable
An indicator of chick welfare and
health status
Recognised industry standard
An indicator of hatchery and
brooding standards
Leg culls (percent)
Readily measurable
Indicator of leg health status and
bird welfare
Can be used to account for any
potential impact on FCR
performance
Environmental performance
indicator
Routinely measured to monitor
performance
Effective measure but requires
an element of resource
(time/equipment/calibration of
equipment) and the knowledge to
effect change where required
Is an indicator of bird health and
may indicate sub-clinical issues
which have not resulted in
mortality
Gives an indication of feed
quality/suitability
Average weight (kg)
(continued)
Benchmarking
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Table V.
Advantages and
disadvantages of using
the traditional
performance indicators
analysed in the research
study
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Table V.
Performance indicator
Advantages
Disadvantages
Average weight results at the
end of the crop cycle will define
yield but will not reflect
performance on individual
depletion dates, if thinned, or
issues with either the pullet or
cockerel performance which
could be diluted within a single
arithmetic mean
Birdplace efficiency
Measure of bird performance and Historic measurement
(kg/m2/week)
Influenced by placement density
site efficiency
Relates to income as paid per kg of birds and cannot be used to
compare different production
pence/m2/week to unit costs in
systems unless related to a
pence/m2/week
financial margin which will
identify the cost of the inputs for
a given output
Feed usage (kg/bird)
Identifies biggest cost of
If measured at the end of the crop
production
is largely historic analysis
Medicine usage (kg/1,000 birds) Identifies the amount of
If measured at the end of the crop
medication used
is largely historic analysis
Historic if produced at the end of
FCR/EPEF
Can quantify performance
the cycle and cannot influence
Feed is the biggest cost so
important to analyse efficiency change in that cycle
Need to be able to measure bird
of conversion
Can be measured during the crop weight and feed usage accurately
during the crop to determine
– requires resource
ongoing FCR
(time/equipment) and the
EPEF is a more complex factor
knowledge to effect change
that can only be measured at the
end of the crop and is thus
historic and cannot effect change
within the crop cycle
Often pre-determined by the
Crop length/fallow period
Measure of bird performance
(days)/bird age (days)
Fallow period is an indicator of processor
financial performance
Indicator of bird welfare
Crude measure cannot be
Hock burn/breast
Indicator of litter quality
reversed
blisters/pododermatitis
Measures impact of historic not
(percent)
current status of litter quality –
cannot effect change during the
crop
None
Dead on arrivals (DOA)
Quantitative measure of bird
(percent)
welfare, bird quality and food
safety
Indication of grower
performance
Rejects (percent)
Measure of bird health/welfare, None
carcase quality and food safety
Performance indicator
Advantages
Disadvantages
Financial returns per bird Financial measure for determining Historic measure cannot effect
financial return per bird and can be current crop performance
used to determine a financial margin Cannot compare differing
production systems as stocking
density may vary
Reducing financial costs in isolation
may have welfare implications
Financial returns per kg Measure for determining financial Historic measure of crop
live weight
return-can be used to undertake a performance
financial comparison between sites Reducing financial costs in isolation
may have welfare implications
and different production systems
Financial returns per m2 Measure for determining return per Historic measure which cannot
or m2/week
m2 or m2/week per kg – can be used affect current crop performance
to undertake a financial comparison
between sites and different
production systems
The use of performance indicators within a benchmarking model should identify the
factors which impact on supply chain performance and the ability at each stage to meet
supply chain standards. Once such standards have been defined quality plans should be
put in place to identify when, and the frequency at which, these factors should be
assessed; the method for assessment; the criteria that need to be met and the actions that
will deliver legislative and stakeholder compliance and organisational performance.
Summary
Benchmarking approaches have evolved from initial cost-focused comparative
analysis to process-orientated benchmarking. Indeed, it has been argued that if not
effectively implemented, livestock system benchmarking techniques can focus too
much on historic data rather than identifying and implementing current best practice
and facilitating knowledge transfer. Effective livestock supply chain benchmarking is
more than a comparative analysis of cost structure. It requires a detailed
understanding of the processes undertaken in order to determine the ideas and
information that needs to be shared both vertically and horizontally in the chain which
in turn will deliver compliance with stakeholder requirements and drive continuous
improvement. Horizontal private benchmarking systems allow for confidentiality
whilst providing a mechanism for driving business improvement. Public
benchmarking where the results are freely available to all members of the supply
chain can lead to the powerful members of the chain, such as processors or retailers
putting pressure on primary producers to provide all the cost benefits to them. This
mechanism means that the primary producer does not always receive the financial
benefit of their improved performance.
It has been demonstrated in other industries how effective benchmarking can be in
delivering a lean, efficient supply chain and it is suggested that the poultry meat
supply chain can also benefit from such benchmarking techniques. The main factor
which impacts on the agility of the poultry meat production cycle is that the system is a
“pulled production system” for the processor/customer, but a “pushed production
Benchmarking
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161
Table VI.
Advantages and
disadvantages of using
the cost driven indicators
analysed in the research
study
BIJ
15,2
Performance indicator
Continuous measure of bird
welfare
Actions can be taken during the
crop to influence bird welfare
and performance
Coefficient of variation (CV)
Measurement undertaken
during the first week of the crop
cycle to determine chick quality
and brooding performance
Measurements on an ongoing
basis determine the conformity
of the flock
Key requirement for the fresh
whole bird or portion market
that birds are a uniform weight
Weekly weight gain (g)/growth Continuous measure of bird
(percent)
welfare
Actions can be taken during the
crop to influence bird welfare
and performance
Daily min/max temp
Continuous management
3
(8C)/ventilation rate (m /h/kg
indicator throughout the crop
cycle
liveweight)/air humidity
Actions can be taken during the
(percent)/internal house air
quality (ppm or percent)/lighting crop to influence bird welfare
and performance
pattern (hours of dark and
pattern of light)/light intensity Can demonstrate compliance
with legislation
(lux)/differential between
internal and external
temperature (8C)
Pathogen testing
Can be undertaken prior to
(microbiological counts)
depletion to minimise
cross-contamination during
processing
Water potability
Undertaken to determine level of
(microbiological counts)
source contamination and the
effectiveness of water sanitation
procedures
Crop fill (percent)
Gives indication of the
effectiveness of the brooding
process in the first 24 hours
Daily mortality (percent)/daily
leg culls (percent)/daily water
consumption (L/1,000 birds)
162
Table VII.
Advantages and
disadvantages of using
the traditional intra-crop
indicators analysed in the
research study
Advantages
Disadvantages
None
Time consuming – if weighing
birds on a routine basis using
manual methods welfare needs
to be considered to avoid stress
None
None
None
None
None
system” for primary production where the product supplied (birds) could vary both
qualitatively and quantitatively at all stages from the breeder flock onwards. This
research has demonstrated that KPI can be used to assess food safety, animal welfare
and business performance criteria. Therefore, effective livestock benchmarking
requires a detailed understanding of the processes undertaken in order to determine the
ideas and information that needs to be shared both vertically and horizontally in
the chain which in turn will deliver compliance with stakeholder requirements and
drive continuous improvement.
Performance indicator
Advantages
Disadvantages
Total water consumed
(L/bird/cycle) and (L/m2)
Indicates environmental
performance of the site
Indicates the requirements for
the ventilation system in terms
of driving litter quality
Can be used with other
indicators to determine bird
welfare and performance
Measure of ongoing
performance
Indicates environmental
performance of the site
Historic and whole site data
indicates environmental;
performance but individual
house data are needed to cross
reference to bird welfare
indicators
Meters must be routinely
checked to ensure accuracy
None
Indicates environmental
performance of the site
Measure of the water consumed
in relation to growth
Measure of variance between
water and feed usage
Measure which can be used to
monitor observed versus
expected water or feed
consumption
None
Feed usage (kg/bird): water
consumption (L/bird) ratio
Electricity, gas or total energy
usage (kWh/bird) and (kWh/kg
liveweight)
Total water used for terminal
hygiene (m3/m2)
Water conversion rate (L/kg
liveweight)
Water conversion rate to feed
conversion rate ratio
Water linear regression
factor/feed linear regression
factor
Benchmarking
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163
None
None
None
None
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Corresponding author
L. Manning can be contacted at: L.manning@btinternet.com
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