POULTRY SUPPLY CHAIN: A SYSTEM APPROACH
Mohammad Shamsuddoha
PhD Student, Curtin University
And A/Professor of Marketing, University of Chittagong, Bangladesh
78 Murray Street, Perth, WA 6000
Telephone: +61425432360, E-mail: mdsdoha@ gmail.com
Mohammed Quaddus
Professor, Curtin Graduate Business School
Curtin University, Western Australia
Desmond Klass
A/Professor, Curtin Graduate Business School
Curtin University, Western Australia
ABSTRACT
Supply chains of individual farms linked with final market via intermediate companies are
becoming a normal business phenomenon. Yet at present, it is not clear how such supply
chain networks can achieve stability/sustainability in terms of structured network to gain
benefits, meeting demand and supply and achieving highest productivity. This quantitative
study investigates these questions using a production process simulation model of a poultry
parent stock farm and its forward and reverse chains. The model was developed in a system
dynamics simulation environment using a design science methodology. Model analysis shows
that intricate poultry supply chains behave inconsistently over time to meet market demands.
This paper also focuses on poultry unused wastages. The objectives of this study are to find
out social, economic and environmental benefits through forward and reverse poultry supply
chains.
Keywords: Reverse logistics, Systems thinking, Poultry, Bangladesh
INTRODUCTION
Bangladesh poultry plays an important economic role for 73% of rural people who lives in
tural areas (Reneta 2005). The poultry industry gained more than 200 percent growth in the
last 5 years with a number of identified problems (Shamsuddoha and Sohel 2004).
Bangladesh Poultry is dominated by backyard local chickens (Desi or local) in scavenger
system (Nielsen 2007) followed by commercial farming. Poultry helps the livelihood of many
and contribute towards improving the family diet with eggs and meat (Das et al. 2008) and is
a cheap source of animal protein in terms of meat and eggs (Shamsuddoha 2010a). Poultry
meat alone contributes 37% of the total meat production in the country and 22 to 27% of total
animal protein (Ahmed 1988, Haque 1992) producing 11500 metric tonnes of chicken meat
(FAO 2003). Availability of poultry meat in Bangladesh is only 16.5 gram/day (Amin 2005),
whereas world per capita consumption is 30.14 gram/day and 95.89 gram/day for USA
(Farrell 2003). Nutritional deficiency is very severe among the rural people due to the scarcity
of animal protein. To reduce poverty and improve nutritional status, poultry can play a
significant role in the subsistence economy of rural people as it provides self-employment for
unemployed people (Hai et al. 2008). There is significant scope to establish and produce more
poultry meat and chicks to balance the protein supply and intake.
Recent turbulent business environments pushed the business-owners to collaborate with
various forward and reverse chains and as well as with individual companies. This is a
smarter way of getting sufficient strength to compete and sustain existing competitive market.
Often, these kinds of collaboration and networks are very successful, or at least as successful
as their vertically integrated counterparts (Coase 1937, Williamson 1975). This study
considers a Bangladeshi poultry case industry with forward and reverse chains. Multiple
chains generate various individual small-medium business of poultry parent stock and
diversified products of day old chicks (DOC), mature chicken, eggs, and different kind of by-
products. Adapting or controlling multiple chains is purely market-driven. This network
begins from raw material collection to final products and includes parties like producers,
processor, distributor, supplier and retailers. According to the owner of the case industry and
other stakeholder companies stated their frustration on understanding market mechanism.
Market mechanism consist of proper policy adaptation, uncertain calamities, seasonal demand
and supply variability, disease, government inability to take action, cultural constraints and
the likes (Rahman 2013, Mannan 2013). Inabilities to projection of market variability in terms
of supply-demand and proper wastes management are the main problem of this industry.
The first objective of this research is to model poultry forward and reverse supply chain.
Second objective is to use various poultry wastes for making valuable by-products and
integrate with the main supply chain for the sake of achieving social, economic and
environmental benefits. There are numbers of poultry wastes generated in the poultry
operation and thrown into vacant land and rivers which then cause severe environmental
damage (Shamsuddoha 2011a, b). In this study, the researcher identifies the benefits from
reversing poultry wastes to the addition production process of by-products. This by-products
processing brings social, economic and environmental benefits to the society. The supply-
demand issue in poultry is a complex cycle in Bangladesh. There are a number of calamities
and policy constraints involves with the poultry supply chain, namely disease, natural
disaster, political unrest, government policy, finance and over/under production. These all
happen due to an unorganized supply chain as different people are involved in different part
of the chain. There is no proper coordination so farmers and producers lose opportunities to
do business. Given this, the final objective of this paper is to develop a model which has a
smooth supply chain by combining all the processors in one frame. A simulation supply chain
poultry model was developed based on the in-depth interview with experience poultry
businessmen and executives. Both forward and reverse supply chain have been incorporated
in the model. The motive of this research was to find out the solution of the mentioned
objectives by simulating a poultry production process. To validate the simulation findings, an
empirical example is presented and compare with the model output.
LITERATURE
Poultry Industry in Bangladesh
Bangladesh has a long history of poultry rearing under traditional backyard farming practices
(Reneta 2005). Poultry is dominated by backyard local chickens (Desi or local), which mostly
survive through a natural scavenger system (Nielsen 2007). Livestock sub-sector of Poultry in
Bangladesh is playing important role to its economy in light of growing small business, cheap
sources of protein supply (Shamsuddoha 2010b), and providing a livelihood for millions of
people (Shamsuddoha and Sohel 2008). But practically, this industry was unsuccessful in
adapting the latest technology for poultry processing and procuring in a sustainable way
(Corbett and Kleindorfer 2003). The poultry supply chain is a deep rooted connection among
raw material supplier, breeder, broiler farmers, processor, distributor and final consumers.
Though Bangladesh poultry has invested and trades in millions of dollars, it does not have a
structured supply chain (Shamsuddoha 2012). Accordingly, this industry is missing the
opportunity to do better business than what they are currently doing. Bangladesh poultry need
an effective structured supply chain model that practices sustainability, efficient supply chain
processes, environmental issues, profitability and optimality concepts (Shamsuddoha 2012).
Forward Supply Chain
The forward Supply chain (FSC) is the process that starts from raw materials collection to the
final consumption of the finished product (Cox, Blackstone, and Spencer 1995). It also links
together the internal and external partners of suppliers, carriers, investors, policymakers,
intermediaries companies and information systems providers. A key point in supply chain
management is that the entire process must be viewed as one system (Lummus and Vokurka,
1999). In summary, the forward supply chain is a step by step process of converting raw
materials to finished goods (Kocabasoglu, Prahinski, and Klassen 2007). In the same way, the
poultry forward chain start with collecting parent stock breed followed by collecting
hatcheable eggs from parent breeder, hatch the eggs in the hatchery, distribute it to farmers
through middlemen, rearing them for certain time by the ultimate farmers and selling meat
and eggs to the ultimate customers. The smoother the supply flow is, the more benefits start
to come to relevant companies to achieve sustainability.
Reverse Supply Chain
Recently, supply chain (RSC) was a step towards integrating the issues of disposal, recycling;
and remanufacturing of reject wastages or products (Kocabasoglu, Prahinski, and Klassen
2007). It includes the consideration of product re-design, manufacturing by-products, by-
products produced during product use, product life extension, product end-of-life, and
recovery processes at end-of-life (Linton, Klassen, and Jayaraman 2007). Experts are calling
it a reverse supply chain which has become an area of academics over the last two decades
(Tibben-Lembke and Rogers 2002, Stock and Mulki 2009). An inspiring review work in the
supply chain was published in the early nineties by the Council of Logistics Management
(Stock 1992), while Carter and Elram (1998) have traced indications of scientific interest in
the field to the early seventies. Reverse Logistics (RL) is associated with a holistic set of
activities like recycling, repair, reuse and reprocessing, as well as collection, disassembly and
the processing of used products, components and/or materials (Kokkinaki et al. 2001). It is
evident in the literature on the automobile industries, electronic goods (such as cell phone),
paper recycling, sand recycling and even carpet recycling industries, all of which display high
percentages of product retumn and hence room for optimal and eco-efficient policies
(Aghalaya, Elias, and Pati 2012). Like the above industries, poultry industry generates tonnes
of wastes like litter, waste feed, feather, reject eggs, intestines etc. (Shamsuddoha 2011a).
There is little literature dealing with poultry wastes reusing or recycling. This research
demonstrates the benefits of further usage of poultry wastes which farmers used were dumped
into river water and vacant land.
Combined Forward and Reverse Supply Chain
The authors interviewed case poultry farm owner and through that came to an understanding
of Bangladesh poultry forward and reverse supply chain. In forward chains, Bangladesh
poultry starts from rearing grandparent breed followed by parent stock farm or breeder farm,
hatchery, distributor, broiler/layer farms (day old chicks’ consumers), wholesaler, retailer and
processor. Day old broiler chicks (DOC) are supplied to the distributors to distribute towards
ultimate farmers who produce meat and eggs for mass people. DOC becomes mature
chickens which are ready for supply to the open market, restaurants and processing units. In
this whole process, a number of issues can be accommodated such as economic, social and
environmental issues. Each of the issue covers number of concerns; for example, employment
generation under social issue. The reverse supply chain is a relatively new concept to deal
with product return, recycle, reuse to keep the environment by using industry wastes
(Shamsuddoha 201 1a). Realistically, poultry industries have no chances of product retrieval,
retum or reconditioning due to its perishable nature. However, there are immense
opportunities to reuse or recycle poultry wastes. By reusing poultry wastage, industries can
make valuable products like fertilizers, biogas, pillows, charcoal, and bakery items
(Shamsuddoha 2011a). It was also evident from the in-depth interview that various kinds of
poultry wastes are generated in the poultry process namely litter, feed waste, feathers, broken
eggs, rejected eggs and intestines. Poultry litter can be used for producing organic fertilizer,
bio gas, artificial charcoal and fish feed; feathers can be used as raw materials for the bed
industry; reject eggs can be used for the bakery industry; and broken eggs and intestines can
be used for fish feed (Shamsuddoha 2011b, Shamsuddoha, Quaddus, and Klass 2011a). The
above review of early studies showed that there was little evidence of research on combined
poultry forward and reverse supply chains for social, economic and environmental benefits
for the society.
SYSTEMS THINKING AND MODELLING METHODOLOGY
System thinking is an understanding of how a particular system works with number of
influences. Initially, Forrester (1961) defined the Systems approach and its complexities
related to managing supply chains in the mid-1900s. The majority of research touched on
forward supply chain and only a few conducted research on reverse chains (Aghalaya, Elias,
and Pati 2012). Systems modelling approaches can be useful for the analysis of relevant
dynamic simulations of such feedback loops in a system. Examples of the applications of
system dynamic modelling touched in the automobile industry (Sterman 2000), the paper
recycling industry (Spengler and Schroter 2003), the poultry industry (Shamsuddoha, Klass,
and Quaddus 2011, Shamsuddoha, Quaddus, and Klass 2013, Shamsuddoha, Uddin, and
Nasir 2013, Aghalaya, Elias, and Pati 2012, Shamsuddoha 2011a, b, c, Shamsuddoha,
Quaddus, and Klass 2011a, b) to the name of few. There is an immense scope for utilising
systems thinking in this research effort, which also incorporates economic, social and social
aspects in forward and reverse supply chains. Forrester’s (1961) famous “Beer Game”
(Sterman 1989) and Meadows’s ‘’hog cycle’’ (Meadows 1970) were developed on the basis
of mentioned problems that have been dominant in our understanding of amplification effects
in supply chains. Methodologically, system dynamics has the ability to deal with complex
dynamics systems. That is, due to the complicated factors and non-linearity behaviour among
variables and the dynamic behaviour of complex systems are difficult to predict from a
description of their static structure. Hence, simulation modelling and analysis is essential for
robust policy design (Sterman 2000).
Phases
Steps
Problem Structuring (Aghalaya, Elias, and
Pati 2012, Maani and Cavana 2007)
Behaviour over time graph development
Identify Variables (Aghalaya, Elias, and
Pati 2012, Maani and Cavana 2007)
In-depth interview
Causal Loop Modelling (Wolstenholme
1990, Sterman 2000)
Variable identification and
Causal loop model development
Draw Quantitative Simulation Model with
rate, level and constant variables
(Wolstenholme 1990, Sterman 2000)
Sketch the model based on relationship among
variables
Run Simulation
Entered real life data once with starting variable
Validation and Reliability (Barlas 1996)
Examine structural validity and assess the data
reliability in different phase
Test Extreme Condition (Barlas 1996)
considerable Changes of key variable values to
observe output reliability
Forecasting Future
Model run for 300 weeks whether it has only
104 weeks data to compare with the reality.
Table 1: Methodological Framework
The methodological approach of this study is based on the Systems Thinking and Modelling
methodology (Maani and Cavana 2007). The two phases of this methodology used in this
study follow a qualitative approach through depicting causal relationship among variables
(Sterman 2000), as shown in Table 1. In the first phase, the complex problem related to
product returns in the poultry industry was structured systemically. For structuring the
problem systemically, a behaviour-over-time information and chart was developed. In the
second phase, a causal loop model was developed using a rigorous simulation package of
Vensim DSS 6.01b.
Both primary and secondary information was used in this study. Primary information was
collected in September 2012 mainly through in-depth interviews with the sample respondents
from the poultry case industry. This research used in-depth interviews and observations to
gain insights to help develop a poultry supply chain model. The total respondents included the
top five executives and case farm owners who were interviewed. Secondary information was
collected from various books, referral journals, conference papers, statistical yearbooks and
company record and reports. This study adopted a positivist ontology, empirical epistemology
and quantitative methodology based on real supply chain cases of poultry processes. The
design science methodology was chosen for this study as models were developed with
relevant variables to attain goals (Simon 1969) and it can be hard and soft to meet particular
objectives (Venable 2006a, b). A simulation package of Vensim DSS 6.01b was used as a
tool to analyse poultry processes in order to investigate the research objectives.
Supply Chain Modelling
Poultry supply chain in Bangladesh is suffering due to improper forecasting of demand and
supply (Rahman 2013). Policymakers and farmers have failed to incorporate market driven
knowledge into production or process level of action. Calamities and policy related matters
are untraceable for the poultry producer. Management science for a long while has
considered supply chains as an undesirable real-world abnormality, rather than a successful
business model (Thomas and Griffin 1996). In this situation, mathematical analysis like
simulations can show future projection based on historical abnormality in supply chains,
where actors will strive to optimize their performance from a central position (Thomas and
Griffin 1996, Sarmiento and Nagi 1999, Akkermans 2001). Few writings have investigated
optimal policies for supply chains as a result of these chains proving successful (Cachon
1999, Gavirneni, Kapuscinski, and Tayur 1999, Lee and Whang 1999, Chen et al. 2000). We
suggest that the poultry industry should be operated and evaluated based on separate supply
chain to enable that the policy makers to design future production, demand and supply
effectively.
Problem Structuring
In the problem structuring phase, a behaviour-over-time graph and table was developed for
the case industry. Developing a ‘reference graph’ or historical trend graph is one of the tools
used in systems thinking (Aghalaya, Elias, and Pati 2012) to show the real life patterns of the
main variables in a system over an extended period of time. Typically, the data or
information can be taken for several months to several years. The more historical data
gathered, the better the extrapolation of future trends can be predicted. Such pattems can
indicate the variations and trends in the variable of interest, for example, growth, decline,
oscillations, or a combination thereof. The important elements captured by a graph are the
overall trends, directions and variations, not the numerical value of the variable (Aghalaya,
Elias, and Pati 2012).
900000
800000 Parent Chicks
700000 f~ py LL —Parent eggs
600000 -, —— Chicks
300000 ——Broiler
400000
—— Employment
300000
200000 $F —Biogas
100000 ——Fertilizer
° === Fish Feed
Fonagaenegregreyegae
ANAARTRH RRAaas
Figure 1: Draw graph of key variable output over time with realistic data
In this study, reference graphs were drawn (figure 1) to capture the historical output
(behaviour) of key variables. Selected key variables were parent chicks enter, mature parent,
parent eggs produced, employment created, fertilizers and biogas produced, number of
farmers, broiler chicks consumed, and final broiler production. The data input in the graph
covered 104 weeks. It is mentionable that poultry chicks’ production and distribution are run
through weekly cycle. The fluctuation in the individual graph line denotes the variation of
production and distribution over time. For example, parent eggs line fluctuate a number of
times in 104 weeks. Lots of ups and downs can be found in one line (random fluctuation)
whereas drastic fall and rise means either collapse or sudden rise of the market and represent
fluctuations of business caused by demand-supply gap, over-under production, calamities,
and policy barriers. This research focused on discovering these problems through
predicted/simulated future results to take appropriate measures.
CAUSAL MODEL BUILDING
Recently, causal loop diagramming formed an important part of a system dynamics model.
Positive and negative feedback loops are the building blocks of system dynamics and causal
diagram led to the conceptualized a prospective model (Richardson 1986). A causal loop
diagram also provided the visualization of how interrelated variables affect one another. The
diagram consists of a set of nodes representing the variables connected together (A ghalaya,
Elias, and Pati 2012, Maani and Cavana 2007). The relationships between variables,
represented by arrows, can be labelled as positive or negative. To generate a directed arrow, a
positive (+) sign near the head of the arrow indicates that an increase (or decrease) in a
variable at the tail of an arrow caused a corresponding increase (or decrease) in a variable at
the head of the arrow. If an increase in the causal variable caused a decrease in the affected
variable, a negative (-) sign was placed near the head of the arrow (Aghalaya, Elias, and Pati
2012).
Figure 2: Causal Diagram of Poultry Supply C hain Model
The above figure shows the relationship or link between/among key variables that reflects
reality includes only the key variables that are influential and have an impact on the outcome.
Figure 2 show a numbers of loops in this qualitative/causal model. These loops includes:
Negative Feedback loop ‘Parent Chicks’ and ‘Mature Parents’: If parent chicks supply
increases then mature parent will increase as well. But when mature chicks’ increases, parent
chicks will decreases for the time being until flock finish its cycle.
Positive feedback loop ‘Required Employment’ and ‘Total Employments’: If required
employment increases, total employment will increase. Again, when total employment
increases, required employment will go up as well.
Positive feedback loop ‘Total Chicks Supplied” and “Total Broiler Production”: These
two have the positive relation with others to increase based on other variable.
QUANTITATIVE SIMULATION MODEL
The model below was developed based on a causal diagram and the relationships found
among or between variables. This complete model incorporates input, output and process
information for individual constants, auxiliaries and level variables. The model also consist
number of reference variable and excels lookup to compare and contrast it with real life
output. This is shown in a different graph provided below.
Govt Poy
Compete Actin
Tipe! Minna
ee Enplomets
*enployment at
Spey Cotaler
Denand Contr
/
aly th Yaka ag te
pegs Fem Fes Fame FOE
Figure 3: Poultry Supply Chain Simulation Model
Model starts from the “Broiler Chicks Lookup” variable where policy maker/farmers decide
how many breeders can be reared in their whole process as a flock. All square boxes denotes
level variable which plays an important role in the poultry supply chain. This breeder chick’s
lookup is the only information given as input which was taken from the case farm.
RESULTS
Validity and Reliability
Validity and reliability tests builds confidence in system dynamics models (Barlas 1996,
Forrester and Senge 1980) as part of the behavioural validation. The models of this study
were tested and validated based on the formal criteria by Barlas (1996) and Forrester and
Senge (1980), which involved three major stages: structure validity; behaviour validity and
tests of policy implications. Basically, structure validity tests purposes at assessing model
structure and parameters without examining relationships between structure and behaviour.
Structure validity comprises a structure-verification test, extreme-condition test, dimensional
consistency test (Barlas 1996). Structure validity has been checked by “Check Model” and
“Check Units” option of the research tool. The authors compared this with the real life
variable structure formation and with model objects. In behaviour validity tests, we look at
whether the model variables behave with each other consistently or not. In addition, it
examines uneven behaviour in extreme or unusual condition (figure 5). This model was tested
using various changing policies. The model was also tested using various policies by the
policy makers. For example, policy makers want to get a growth of 30% instead of 20% due
to demand increase. This change was made to see the immediate effect on other variable as
well as whole model as a result of the change. Figure 4 presents an example of the reliability
check by comparing model result with actual result. Most of the results are within the
acceptance level.
i io Parent Eggs Comparison
Peet
——al 800,000
ee 600.000
iT 15 & i
‘Ma Es ©
400,000
0 10 62030, 4080s i 100110
Time (Week)
Parent Eggs in: Current. —t——+-—++-_—_++-__+-—__ ++ Real Eggs Excel : Curent ———2-——3-_— 3-3 3——
Broiler Chicks Comparison
Figure 4: Few tests summarizes for reliability test.
Figure 4 shows the simulated output of seven key variables in the poultry industry. The blue
lines are marked as simulated result and red line marked as real life data. It shows that various
lines are almost matched with each other having around ten percent fluctuation. The
simulation was used to predict the next four additional years result/output based on historical
business wave. This could allow producer and policymakers to make predicts a policy and
decision they used for certain change.
(p.9 ] Parent Eggs
800,000
gono00 Cee a apa Te Prt
0
30 60 90 120 «150 «6180 §=6.210 30 240.) 270) 300
2
|
Time (Week)
Parent Eggs in : Curent’ ttt ++ Real Eegs Excel : Curent —2——2-——3-__3-—_—>—
‘Market Demand
Broiler
= 700,000
aa 2 300,000
- 0 60 90 120 150 180 210 240 270 = 300
ings Time (Week)
‘Total Broiler Production : Cunent j++ Resi Marue Broiler: Curent @4——B-_-s-_s-—_s—
| pel
————
Biogas
‘Growth Type?
cubic metre/ Week
final
2°
3S
o 68
0 30 60 90 120 150 180 210 240 270 300
Time (Week)
Biogas: Conn, $f} t+ ++ Rat! Biogss Production : Curent —2——2-——3-—3-—
Figure 5: Extreme Condition test of key variables
Validity of model equations under extreme conditions resulting values beyond the
projection/anticipation of what would happen under a similar condition in real life (Forrester
and Senge 1980). Figure five demonstrates the extreme condition test of the simulation
model. So far the results fluctuates based on extreme conditions (such as 10% decrease of
Government policy, 25% of increase of competitors action and 15% decrease of natural
disaster, demand fall 25%, 10% decrease for poultry disease etc.) are not showing
abnormality. The model behaves perfectly based on changes of policy and constant variable.
Forecast and Variable values
The main problem in the Bangladesh poultry industry is in its inability to forecast or predict
existing and future demand of the market. Demand-supply forecast mostly rely on
uneven/uncertain calamities of disease, natural disaster (flood, cyclone, etc.) and finance.
Figure 5 to 8 shows the forecasted output for supply demand gap, employment creation, and
benefits from reverse supply chain. Biogas, fertilizer and fish feed are the main by-products
coming from the reverse chain using existing poultry wastes. As a starting position, no
calamities and policy problems were included. There were adjusted variable as a part of the
analysis. For example, in figure 5, natural disaster slide down to 0.85 from 1, which means
production process, has lost 36% business due to natural disaster. And more importantly,
maximum and minimum value has set for this natural disaster variable is 1 and 0 respectively
which is logical too.
Employment Generation through Forward Supply Chain
When an | industry operates successfully, it creates so many opportunities for the people who
Fa 7
TL 2s Employment Analysis
Tine to Hie Employee
nee
Time to Minimom
BGER
o——
al 4 975
Real x 5
me Employment ac eee — 2
o e Rate Ea
ol im BGER——%, --DHEmP! pier 2
FFER BC Eel 2 750
———— =
ol i =
— 5
= a
ol ia] 625
BEER,
———S
ol al
BER 500
———>—
eT MW 0 20 40 60 8 100
aa Time (Week)
o“——
ol hh Hire Rate : Current. —+-——t—+-—_+-_4+—
Total Employments : Current. #—#—2—
Tl Th Real Current
DEER
dealers appointed. All of them controlled from the
co ae
‘i
Salas Team Performance | CECLo
i
ae
—
mil lz
eal
eae * Page
—
atch \ [Be
‘Sopply Controller
ra
‘Dist
central head office marketing unit.
Broiler Supply Demand Gap
700,000
475,000
250,000
25,000
-200,000
40 60
Time (Week)
Total Chicks Demand : Current. -<-——+——+—+—+_
Total Chicks Supplied : Curent —2-——2—2— 2
Supply Demand Gap : Curent —————
80 100
Figure 7: Supply Demand gap analysis for poultry meat
Reversing Poultry Wastes for Producing By-Products
Figure 8 shows the process of by-products coming from poultry wastes through reverse chain.
The figure shows the output of biogas, fish feed and fertilizers. It is important to mention that
there are few other poultry wastes generated in the poultry process namely, intestines,
feathers. Intestine can be used for making fish feed and feather can be used for pillow-bed
and for the sophisticated plastic making industry. This area is a key for generating more
economic, social and environmental benefits for farmers, processors, distributors, society in
particular and country in general.
ao Sn Eo Fish Feed
FFW sate = 4 7 oy
4
Foo a] 025 at} ‘ 0 20 40 60 80 100
Fish Feed Conv = Q : :
Fish Feed on Fat Cone Time (Week)
i‘ ish % are DO, Ua
Pre 2 oe Te Con Fate Real Fish Feed Prodoction : Curent 2—2—B—
Fertilizer
Tons
OS aa
——
010.6 i
BGW a 0 20 40 60 80 100
Time (Week)
171 i Fenliser Cumene $+} +3
Biogas Rate Real Fertilizer Production : Curent —2—2—2—>
Biogas
Z 6,000
:
3 0
0 10 20 30 40 50 60 70 80 90 100 110
Time (Week)
Biogas : Current ‘Real Biogas Production : Current gnome
Figure 8: Poultry reverse chain
CONCLUSIONS
Most of the poultry industries in Bangladesh are medium in size having unorganized supply
chain network with lack of coordination among stakeholders. Industries are always concerned
about the high cost of deployment associated with forward and reverse supply chains.
Dynamic process, technology, infrastructures and whole supply chain network management
involves huge financial capital which is almost unbearable to achieve in Bangladesh.
Bangladesh poultry can integrate complete supply chain network so that they can compete
with the local and global market and also reduce their operation cost. The model in this
research shows the way of the integrating forward and reverse supply chains. It is evident
from the model that reversing poultry wastes for processing by-products can give number of
benefits to the industry, society and environment. Integrated forward and reverse processing
brings more economic (monetary), social (employments, new business) and environmental
(free from pollution) benefits to the relevant society of Bangladesh. Policy variables in the
models are the switches and indicators to study the impact those will have a poultry operation
including various processing. This research is also contributes theoretically in the field of the
commercial poultry sub-sector of Bangladesh. It shows the effective integration of the
forward and reverse supply chains that can be used as a direction or guide for the poultry
producers and related stakeholders. Future research can focus on the different variables and
study its individual impacts on the poultry industry in Bangladesh.
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