CHAPTER 5 A CASE STUDY OF BAMBOO SHOOTS IN...

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CHAPTER 5

A CASE STUDY OF BAMBOO SHOOTS IN THAILAND

This chapter offers a solution for selection of post-harvest

technologies, optimal route selection including risk identification, risk

assessment and risk mitigation for a case study of the Bamboo shoot supply

chain in Thailand.

5.1 RISK INDENTIFICATION AND RISK ASSESMENT FOR

BAMBOO SHOOTS

To cover all possible risk issues in the bamboo supply chain, risk

assessment guideline of World Bank, proposed by Jaffee et al (2010) was

applied for interview and decision practices. The shelf life of the bamboo

shoots depends on storage temperature, storage duration, treatment methods,

packaging etc., and is determined by discoloration, fungal infection etc.,.

Samples of bamboo shoot from farm and wholesale markets were randomly

selected and stored in two different conditions, ambient temperature and chill

temperature (4-10oC).

Table 5.1 Shelf life of fresh bamboo shoot with two storage conditions

SourceAmbient Temperature

(25 – 30oC)

Controlled Temperature

(4 – 10oC)

Farm 5 days 2 months

Wholesale market 10 days > 2 months*

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*It is observed that when the samples were stored for 73 days during experiment, the

appearance attribute was acceptable

As shown in Table 5.1, samples selected from the wholesale market

can last longer than samples selected from farms because samples from the

wholesale market were treated with chemical treatment methods by

commission agents or traders at collection points or wholesale markets, while

samples from farm were not treated. Generally, fresh bamboo shoots from

different farms were picked. At collection points, peeling and trimming were

applied before soaking the shoots in a sulfur dioxide solution. Peeled and

trimmed bamboo shoots were soaked in sulfur dioxide solution (10-30% w/v)

about 3- 5 seconds, and packed in 10 kilogram polypropylene bags, each.

Goods were carried to wholesale markets by pick-up (small truck) at ambient

temperature. To study food safety risk causes by sulfur dioxide (SO2) content

in bamboo shoots, samples from different parts in the chain were randomly

collected and checked for SO2 residual. The results are shown in Table 5.2.

Table 5.2 SO2 residual per 1 kg bamboo shoot

Sources of sampling SO2 (g)

Farms 0.244

Collection Point 92.911

Wholesale Market 13.503

Consumers 1.414

The World Health Organization recommended consumption

quantity should not exceed 0.7 mg per 1 kg body weight. As data shown in

Table 5.2, it can be safe for a consumer if only he consumes fresh bamboo

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shoots from farms. Bamboo shoots must be cooked before consuming.

However, there are many Thai cuisines served with raw items, herring

bamboo shoot for instance. In this case, consumers are at high chemical

hazard risk. To secure profit, post-harvest treatment as a chemical method is

generally applied for bamboo shoots. This is done at the collection point

before shoots are carried to wholesale market. Although this method can

increase chemical hazard risk to consumers, there is no system to prove that

the product is safe or not. Traceability is one vital issue in the quality chain

not yet applied in the case of bamboo shoots. Moreover, if chemical hazard

treatment is not applied, traders believe that the product is in a significant

biological and marketing risk. A trader makes based on his own risk since

browning reaction, growth of yeast and mold and unsatisfactory appearance

occur. These problems directly impact loss for traders and wholesalers. Three

major risk issues in the fresh bamboo shoot supply chain were identified as

shown in Table 5.3.

Table 5.3 List of Identified risks in post-harvest supply chain: a case

of bamboo shoot

Risk Issue Elementary risk Respect to

Product Risk Chemical Hazard Risk Consumers

Biological Hazard Risk Wholesalers and Traders a

Marketing Risk Product Appearance Risk Wholesalers and Traders b

Economic Risk Over cost risk Wholesalers and Traders c

a Biological Hazard Risk is assessed with respect to respects to

wholesalers and traders only because during storage and transportation,

produce can be affected by microorganisms, mostly fungi from outside of

bamboo shoot and lactic acid bacteria from inside of bamboo shoot.

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Moreover, if the produces are stored at the ambient temperature for long time,

bamboo shoots become hard due to lignification process. The lignification

process makes the bamboo shoot into wood or woody through the formation

and deposit of lignin in cell walls. Usually, if bamboo shoot is treated

through chemical treatment, Wholesalers and traders can avoid this risk.

b Product Appearance Risk is evaluated with respect to respect to

wholesalers and traders because mostly this risk occurs during storage and

transportation. When storage temperatures were kept at ambient temperature,

bamboo shoots discolored and were visually unacceptable. Discoloration of

bamboo shoots was attributed to enzymatic browning caused by

phenylalanine ammonia-lyase (PAL) and peroxidase (PO), activated by tissue

injury at harvest. Greater activity of PAL and PO was correlated with increase

of crude fibre and lignin in shoots. Usually, if bamboo shoot is treated

through chemical treatment, Wholesalers and traders can avoid this risk.

c Over cost risk is caused by investment in post harvest treatment.

There are various techniques that can be applied to reduce risk level of

biological hazard risk and discolour appearance risk. However, usually

wholesalers and traders also want to avoid losses due to return can�t recover

investment cost, referred as over cost risk. Chemical treatment is commonly

applied by wholesalers and traders because the cost of implementation of

chemical treatment is very low when compared to other methods.

Risk value or the magnitude of risk events for sustainability is

usually affected by more than one elementary risk factor which is determined

through the recognized cause and effect feedback loop. In Figure 5.1 it is

shown that the magnitude of a risk event has been affected by risk input

factors as an illustrative example. The risk magnitude prediction system is to

determine the magnitude of different risks based on uncertainty level of input

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factors affecting risks. The magnitude obtained from the fuzzy inference

system shows the risk value for sustainability.

Figure 5.1 Quantification of Risk

For the structure shown in Figure 5.1, details of elementary risk

factors shows in Table 5.4.

A commission agent or a trader is a major decision maker involved

in the chain. Frequently both commission agent and wholesaler can be the

same actor who plays a vital role in different activities including selection of

post-harvest treatment. However, application of improper post-harvest

technology to improve quality of products can result in loss of profit due to

price and demand uncertainty. Agricultural producers have little control over

the market forces that drive commodity prices. Supply and demand changes

can cause large and unforeseen swings in prices. Farmers face a variety of

price, yield, and resource risks which make their incomes unstable from year

to year. Farmers deal with a significant level of uncertainty every day. It is

therefore imperative for farmers to manage risk both on the input and the

1. Risk Identification

2. Define Rule for model: Knowledge base in technology, agribusiness, and agro-industry management

3. Fuzzy Inference System (FIS) 4. Elementary Risk Factor

5. Risk Value for Sustainability

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output side. The objective of this part is to understand mechanism and

interaction amongst risks, internal risk factors, risk magnitude, and producers�

revenue in order to mitigate the negative impacts of the risk for farmers.

Interview with the players revealed the major possible risks in the bamboo

shoot supply chain as shown in Table 5.5. Yield loss, over production cost,

and poor appearance were located at important ranks by farmers.

Table 5.4 Risk factors and elementary risk factors with respect to

elementary risk

Elementary Risk Risk Factors Elementary Risk

Factors Fixed and Operation Cost

Complexity Chemical Hazard Risk

Post-harvest treatment

Awareness of consumer

Fixed Operation Cost

Complexity

Biological Hazard Risk


Awareness of consumer Fixed and Operation Cost

Complexity

Product Appearance Risk



Fixed and Operation Cost

Complexity

Over cost risk Post-harvest treatment


Fixed and operation cost, complexity, and awareness of consumer were

the elementary risk factors obtained from interview with fifteen experts(stake

holders or players) involved in bamboo shoot supply chain. Players in the

supply chain are considering these three factors for making decision to

implement post-harvest treatment alternative in long term. For instance, if the

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awareness level of consumer to food safety is very high, then the treatment

method with plausible low cost, and plausible low complexity level which

satisfy quality attribute and food safety will be chosen. But if awareness level

of consumer to food safety is very low, then the treatment method with

possible lower cost and possible lower level of complexity of application that

satisfy only quality attribute will be implemented. Even though the

implementation of second case help in achieving lower cost and lover

complexity level of application, it may contribute hazard risk to consumers.

Table 5.5 List of Identified risk

No. Risks

1 Yield Loss

2 Over production cost

3 Poor appearance

4 Wholesale Price Suddenly Decline

5 Commitment failure

6 Over handling & transportation cost

7 Lignifications during storage

8 Spoilage during storage

9 Demand suddenly declines

10 Retail price declines

11 Insufficient raw material supply

12 Insufficient raw material supply

13 Insufficient Labor

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5.1.1 Application of Fuzzy Logic for Identification and

Assessment

Agricultural producers are forced to make decisions based on

imperfect information. Born out of this uncertainty is the possibility of loss.

A flowchart representing the different stages of the risk management process

carried out by the proposed fuzzy SD approach is shown in Figure 5.1. As

seen in this figure, the dynamic risk management system performs all phases

of the risk management process, i.e. risk identification, analysis, response

planning by the use of the proposed SD-based approach. The complex model

of the risk management process described above was developed employing a

SD modeling software tool known as �VENSIM Plus 5.9e�. The magnitudes

of risks were also calculated using MATLAB software.

A fuzzy inference system is used to determine the degree to which

the inputs belong to each of the appropriate fuzzy sets through the

membership functions. The input is always a crisp numerical value limited to

the universe of discourse of the input variable and the result of fuzzification is

a fuzzy degree of membership. The problem of constructing a membership

function is that of capturing the meaning of the linguistic terms employed in a

particular application adequately and of assigning the meanings of associated

operations to the linguistic terms. The scenario involves a specific knowledge

domain of interest. For this case, fifteen experts were interviewed to elicit the

knowledge of interest and to express the knowledge in some operational form

of a required type.

The magnitude of risk events is usually affected by some risk

factors which are determined through the recognized cause and effect

feedback loops. In Figure 5.2 it has been shown that the magnitude of a risk

event has been affected by four risk input factors as an illustrative example.

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The value of the input factors cannot be known. Moreover, the relationship

between the values of the input factors and the magnitude of risks cannot be

obviously defined.

Figure 5.2 Relationships between the risk input factors, risk magnitude

and consequences

The risk magnitude prediction system is to determine the magnitude

of different risks based on the level of uncertainty of input factors affecting

the respective risk. The magnitude obtained from fuzzy inference system acts

as an input for the simulation of risk consequences in the later stages. The risk

magnitude prediction system includes determination of input factors affecting

the risk magnitude by fuzzy numbers, consolidation of expert inputs by the

fuzzy Delphi technique, and prediction of risk magnitude by the fuzzy

inference system.

The consolidation of input factors was done using the fuzzy Delphi

technique. The final consolidated fuzzy numbers will act as an input to the

fuzzy control system to determine the magnitude of risk events in the later

stage. The inputs (the probability of yield loss and poor appearance) for

simulating the system is computed by the application of fuzzy inference

system. The values of four input factors affecting to risks are proposed by

different experts as triangular fuzzy numbers (TFN) based on their subjective

judgments. The Fuzzy Delphi technique was employed to consolidate the

IrrigationCapital Level

FertilizerApplication

Level

Pre-harvest Treatment Capital Level

BambooClump

Age

Risk Event Magnitude

Consequence of risk

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TFNs provided by the experts. Different components of the fuzzy control

system are determined as follows:

(1) Fuzzification: The membership function values for the four

input variables (i.e. irrigation capital level, fertilizer application level, bamboo

clump age and pre-harvest treatment capital level) and also unit of the input

variables obtained are shown in Table 5.6.

The membership function graphs shown in Figure 5.3, the

membership functions for this study were obtained from the expert. In order

to apply the model to any other supply chain risk project, further work is

needed to establish appropriate membership functions (or rules).

Table 5.6 Input membership function

Operational risk factors

Risk Min Max Most

plausibleUnit

Low Irrigation Level YL 4 30 4 $/Acre High Irrigation Level YL 15 40 40 $/Acre Low Fertilizer Level YL,PA 152 400 152 $/Acre Average Fertilizer Level YL,PA 230 670 450 $/Acre High Fertilizer Level YL,PA 500 758 758 $/Acre Young bamboo clump age

YL 1 3.5 1 Year

Mature bamboo clump age

YL 2 6 4 Year

Old bamboo clump age YL 5 8 8 Year Low pre-harvest treatment level

PA 50 100 50 $/Ton

High pre-harvest treatment level

PA 70 114 114 $/Ton

YL=Yield Loss, PA= Poor Appearance

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(2) Inference: The level of the fuzzy control system�s output

(i.e. the probability of yield loss and poor appearance) is induced by the

inference rules. With four input factors and two output factors: probability of

yield loss and poor appearance, there exist the total of 69 fuzzy control rules.

Output membership function is shown in Table 5.7.

(a) Irrigation capital level

(b) Fertilizer application level

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(c) Bamboo clump age

(d) Pre-harvest treatment capital level

(e) Probability of yield loss/ Probability of poor appearance

Figure 5.3 Membership functions of inputs and outputs

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Samples of Fuzzy input membership function for bamboo clump show

in equation (5.2)

(5.2)

From equation (5.2) ,fuzzy membership functions of bamboo shoot

clump age: young, maturity, and old are derived. The is age of bamboo

clump.

For example, a membership function for a fuzzy set �age� on the

universe of discourse X is defined as U age :X [0,1], where each element of

X is mapped to a value between 0 and 1. This value, called membership value

or degree of membership, quantifies the grade of membership of the element

in X to the fuzzy set age.

Young:

When the age is less than 1year, membership level with respect to young is 1.

If the age is equal to or greater than 1 year and less than or equal to 3.5 years,

membership level with respect to young is . If the age is greater than 3.5

years, membership level with respect to young is 0.

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Maturity:

When age is less than 2 years or greater than 6 years, membership

level with respect to maturity is 0. If the age is equal to or greater than 2

years and less than or equal to 4 years, membership level with respect to

maturity is . If the age is greater than 4 years and less than or equal to 6

years, membership level with respect to maturity is .

Old:

When age is less than 5 years, membership level with respect to old

is 0. If the age is equal to or greater than 5 years and less than or equal to 8

years, membership level with respect to old is . If the age is greater than 8

years, membership level with respect to old is 1.

Fuzzy output membership function for risk magnitude

quantification is shown in equation (5.3) and Table 5.7.

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(5.3)

Definition: a membership function for a fuzzy set R on the universe

of discourse X is defined as µR:X [0,1], where each element of X is

mapped to a value between 0 and 1. This value, called membership value or

degree of membership, quantifies the grade of membership of the element in

X to the fuzzy set R.

Very Low

When risk magnitude is less than or equal to 0.10, membership

level with respect to very low is 1. If the risk magnitude is greater than 0.1

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and less than or equal to 0.20, membership level with respect to very low is

. If risk magnitude is greater than 0.2, membership level with respect to

very low is 0.

Low

When risk magnitude is less than 0.10 or greater than 0.40,

membership level with respect to low is 0. If the risk magnitude is equal to

or greater than 0.1 and less than 0.25, membership level with respect to low

is . If the risk magnitude is equal to 0.25, membership level with

respect to low is 1.0. If the risk magnitude is greater than 0.25 and less than

or equal to l 0.4, membership level with respect to low is .

Medium:

When the risk magnitude is less than 0.30 or greater than 0.70,

membership level with respect to medium is 0. If the risk magnitude is

equal to or greater than 0.30 and less than 0.50, membership level with

respect to medium is . If the risk magnitude is equal to 0.50,

membership level with respect to medium is 1.0. If the risk magnitude is

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greater than 0.50 and less than or equal to 0.7, the membership level with

respect to medium is .

High:

When the risk magnitude is less than 0.60 or greater than 0.90,

membership level with respect to high is 0. If the risk magnitude is equal to

or greater than 0.60 and less than 0.75, the membership level with respect to

high is . If the risk magnitude is equal to 0.75, membership level with

respect to high is 1.0. If the risk magnitude is greater than 0.75 and less than

or equal to 0.9, membership level with respect to high is .

Very High

When the risk magnitude is less than 0.80, membership level with

respect to very high is 0. If the risk magnitude is equal to or greater than 0.80

and less than 0.90, membership level with respect to very high is . If

the risk magnitude is equal to or greater than 0.90 and less than or equal to

1.0, membership level with respect to high is 1.

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Table 5.7 Output membership function

Ling Linguistic

terms linguistic

Trap Trapezoidal

fuzzy number

Very high (0.80, 0.90, 0.10, 1.00)

High (0.60, 0.75, 0.75 0.90)

Medium (0.30, 0.50, 0.50 0.70)

Low (0.10, 0.25, 0.25, 0.40)

Very low (0.0, 0.0, 0.10, 0.20)

From input and output membership function defined, Mamdani FIS

for risk magnitude prediction was developed. Some fuzzy rules used in this

FIS were shown in Figure 5.4. The complete fuzzy rules used in FIS is given

in Appendix 4.

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Figure 5.4 Sample Rules of Fuzzy Inference Systems

Commodity cycles do not only arise in raw material and agriculture

products but also in high-tech and highly differentiated products. System

dynamics (SD) is useful approach in the agricultural commodity supply chain.

In order to study the imprecise and uncertain nature of risk events, fuzzy logic

is integrated to the proposed SD-based approach. The production and

distribution simulation model (PDSM) is needed to act as a baseline in the

risk analysis. There is deficiency of conventional techniques for supply chain

risk management and tools are arising from the multiple feedback processes

involved in the supply chain, as well as the highly dynamic nature of fresh

produce supply chain. The complementary tool is required. The system

dynamics approach is an alternative tool that enables one to consider these

issues in a SD environment. PDSM for risk analysis was developed in a SD

environment.

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5.1.2 System Dynamics Modeling

Average production is 80,808 tons per year (2005-2008), while

produce is supplied to wholesale markets, retailers, and factories. Two

significant risks are yield loss and poor appearance. These were selected to

quantify inputs and develop risk models through the fuzzy SD approach. With

random age of bamboo clumps, farmers are required to decide appropriate

capital levels for irrigation, fertilizer, and pre-harvest treatment. These

internal factors are considered by farmers as the main risk factors affecting

their return. Furthermore, for manufacturers, wholesalers and retailers,

post-harvest treatment with respect to the desired market is required. The

harvest season of bamboo shoots starts from March and lasts to November.

Apart from production input, production yield and price depends on season.

Both influence interaction. In order to quantify risk magnitude for yield using

the fuzzy inference system and predict revenue through system dynamics

simulations reasonably, we divided the harvest season into four periods. There

are D1 (March-April), D2 (May-June), D3 (July-August), and D4

(September-November). System dynamic Simulation model developed for the

case considered and the simulated results are given in Figure A3.1 to A3.8 of

Appendix 3. The equations used in the System dynamic Simulation model for

simulation is given in Table A3.1 of Appendix 3.

5.2 ANALYSIS OF RISK USING SYSTEM DYNAMICS AND

DESIGN OF THE EXPERIMENT

From the bamboo shoot supply chain problem discussed in 5.1, four

policies are proposed for testing as shown in Table 5.8.

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Table 5.8 Four policies (I, II, III, and IV) were tested

Policy Irrigation Fertilizer Pre-harvest

Capital Level

I Low Low Low

II High High High

III Low Average Low

IV High Average Low

The above policies are simulated through the system dynamics

application, Vensim software. Total cost and profit obtained from the system

dynamic models were presented in Table A5.2 to Table 5.13 in Appendix 5.

After the simulation runs, the results were used for factorial design

experiments and Response Surface optimization. The details are presented in

Tables 5.9-5.16.

Table 5.9 Factors and its level for Policy I

Factors Levels Values A: Bamboo Clump Age (block) 7 1.0, 2.0, 3.5, 4.0, 5.0,

6.0, 8.0 B:Irrigation Capital Investment Level

2 4, 30

C: Fertilizing Capital Investment Level

2 152, 400

D: Pre-harvest treatment capital level 2 50, 100

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Policy I: As shown in Table 5.9, to avoid financial risk, all the input factors

were set at a low level. This is the level at which we expect to take on

minimum risk of investment loss in the system. Moreover bamboo clump age

was considered as an uncontrollable factor by setting a block for the factorial

design experiment in randomized complete block design (RCBC). Analysis of

factorial design for policy I is shown in Table 5.10.

Table 5.10 Analysis of Variance for Policy I

Source DF SSE MSE P A 6 807909491 134651582 0.000 B 1 358236799 358236799 0.000 C 1 65033031 65033031 0.000 D 1 33733559 33733559 0.007 B*C 1 160579 160579 0.847 B*D 1 79 79 0.997 C*D 1 2449055 2449055 0.452 A*B*C 1 23633 23633 0.941 Error 42 178851811 4258376 Total 55 1446398037

From Table 5.10, a set of experimental designs have been done in

order to explore the appropriate investment conditions of the three factors.

Irrigation capital investment level (B), fertilizing capital investment level (C)

and pre-harvest treatment capital level (D) are having a significant influence

(p<0.05) on the total profit.

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(a) Main effect plot for policy I

(b) Interaction plots for policy I

Figure 5.5 Main effect and Interaction plots for policy I

Figure 5.5 (a) shows the main effects of the profit in which the

irrigation capital investment level is the most significant influence. With

irrigation capital investment at a low level, it shows that the increasing

irrigation capital investment seemingly caused an increase in profit. Figure

5.5(b) shows two factor interactions in which all cases of interaction do not

significantly influence profit.

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Policy II: All the input factors were set at high levels at which we expect to

take minimum risk for yield loss in the system. This is shown in Table 5.11.

Table 5.11 Factors and its level for Policy II as per factorial Design

Factor Levels Values

A: Bamboo Clump Age (block) 7 1.0 2.0 3.5 4.0 5.0 6.0 8.0

B : Irrigation Capital Investment Level

2 15, 40


2 500, 758

D: Pre-harvest treatment capital level 2 70, 114

Bamboo clump age was considered as block in the factorial design

experiment. Analysis of factorial design for policy II is shown in Table 5.12.

Table 5.12 Analysis of Policy II as per factorial Design

Source DF SSE MSE P

A 6 13466356761 13466356761 0.000

B 1 748318867 748318867 0.000

C 1 318374626 318374626 0.000

D 1 2303402 2303402 0.441

B*C 1 104231974 104231974 0.000

B*D 1 17814 17814 0.946

C*D 1 17020616 17020616 0.041

A*B*C 1 21994 21994 0.940

Error 42 160076471 160076471

Total 55 2697001440

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As shown in Table 5. 12, a set of experimental designs have been

done in the case of policy II in order to explore the appropriate investment

conditions of the three factors. From the analysis shown in Table 5.12, it is

found that Irrigation Capital investment level (B), Fertilizing Capital

investment level (C) significantly influence (p<0.05) the total profit for

policy II.

(a) Main effect plot for policy II

(b) Interaction plots for policy II

Figure 5.6 Main effect and Interaction plots for policy II

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Figure 5.6 (a) shows the main effects of the profit. With irrigation

capital investment at a low level, it shows that the increasing irrigation capital

investment seemingly caused an increase in profit. Moreover Figure 5.6(b)

shows interactions between B and C and C and D significantly influence the

profit.

Policy III: As shown in Table 5.13, the fertilizer capital factor level was set

at average and other factors were set at low levels. Bamboo clump age was

considered as a block in the factorial design experiment.

Table 5.13 Factors and its level for Policy III as per factorial Design


A: Bamboo Clump Age (block) 7 1.0 2.0 3.5 4.0 5.0 6.0 8.0


2 4, 30


3 230, 450,670

D: Pre-harvest treatment capital level

2 50, 100

Analysis of factorial design for policy III is shown in Table 5.14.

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Table 5.14 Analysis of Policy III as per factorial Design

Source DF SSE MSE P A 6 1559220256 259870043 0.000

B 1 1033534685 103353468 0.000

C 2 206522296 103261148 0.000

D 1 13343295 13343295 0.011

B*C 2 133682664 66841332 0.000

B*D 1 4157 4157 0.963

C*D 2 2449055 14130035 0.001

A*B*C 2 28260069 42775 0.978

Error 66 85551 1932655

Total 55 1446398037

As shown in Table 5.14, a set of experimental designs have been

done in the case of policy III in order to explore the appropriate investment

conditions of the three factors. Irrigation capital investment level (B),

Fertilizing capital investment level (C) and Pre-harvest treatment capital level

(D) are significantly influence (p<0.05) the total profit.

(a) Main effect plot for policy III

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(b) Interaction plots for policy III

Figure 5.7 Main effect and Interaction plots for policy III

Figure 5.7 (a) shows the main effects on profit in which irrigation

capital investment level (B),Fertilizing Capital investment level (C) and

Pre-harvest treatment capital level (D) are significantly influencing the

response. Moreover Figure 5.7 (b) shows two factor interactions and

interaction effect between B and C, and between C and D are significantly

influence the profit.

Policy IV: As shown in Table 5.15, the fertilizer capital factor level was set at

average and other factors were set at a high level. Bamboo clump age was

considered as block in the factorial design experiment.

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Table 5.15 Factors and its level for Policy IV as per factorial Design


A: Bamboo Clump Age (block) 7 1.0 ,2.0 ,3.5 ,4.0, 5.0 ,6.0, 8.0


2 15, 40


3 230, 450,670

D: Pre-harvest treatment capital level

2 70, 114

Analysis of factorial design for policy IV is shown in Table 5.16.

Table 5.16 Analysis of Policy IV as per factorial Design

Source DF SSE MSE P

A 6 2.7460E+10 4576646620 0.000

B 1 42438374 424383742 0.000

C 2 1217179237 608589618 0.000

D 1 24928741 24928741 0.352

B*C 2 43998051 21999026 0.465

B*D 1 8605 8605 0.986

C*D 2 14078159 7039079 0.781

A*B*C 2 191140 95570 0.997

Error 66 1875430873 28415619

Total 55 3.1060E+10

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As shown in Table 5.16, a set of experimental designs have been

done in the case of policy IV in order to explore the appropriate investment

conditions of the three factors. Irrigation capital investment level (B) and

fertilizing capital investment level (C) are significantly influencing (p<0.05)

the total profit. While pre-harvest treatment capital level (D) is not

significantly influencing the profit in this policy.

(a) Main effect plot for policy IV

(b) Interaction plots for policy IV

Figure 5.8 Main effect and Interaction plots for policy IV

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Figure 5.8 (a) shows the main effects of the profit in which

fertilizing capital investment level also produced a quadratic effect.

Figure 5.8(b) shows that two factor interactions in all cases of interaction are

not significantly influences the profit.

5.2.1 Optimization to Mitigate Risk to Profit/ Loss

In another set of analyses, response surface optimization was

applied to achieve higher performance of the profit from production and

pre-harvest treatment investment levels. The optimal conditions is shown in

Figure 5.9.

Figure 5.9 Policy Optimization

Figure 5.9 shows that recommended capital investment levels of irrigation, fertilizing and pre-harvest treatment at 40 $/Acre, 455 $/Acre, and 50 $/Ton respectively. This condition was suggested to mitigate risk in the case study. Irrigation capital investment at high level, fertilizing capital investment at average level, and pre-harvest treatment capital investment at low level are recommended. From the Analysis of Variance of Factorial experiments performed for different policies and the main effects plot, the significant factors and the best level for the factors were identified. The details are provided in Table 5.16a.

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Table 5.16a Comparison of different policies

Best levels(values) for the factorsPolicy Significant factor

(B ) (C) (D)

I

Irrigation Capital Investment (B ) , Fertilizing Capital Investment (C) and Pre-harvest treatment capital(D)

30 (High)

400 (High)

50 (Low)

II

Irrigation Capital Investment (B ) and Fertilizing Capital Investment (C)

40 (High)

500 (Low)

114 (High)

III

Irrigation Capital Investment (B ) , Fertilizing Capital Investment (C) and Pre-harvest treatment capital(D)

30 (High)

450 (Average)

50 (Low)

IV Irrigation Capital Investment (B ) and Fertilizing Capital Investment (C)

40 (High)

670 (High)

114 (High)

When comparing the factorial experiment results related to different

policies with optimal result obtained through Response Surface Method, it is

observed that pattern of response obtained through Policy III (Irrigation

capital investment at high level, fertilizing capital investment at average level,

and pre-harvest treatment capital investment at low level) is similar to that of

optimal results achieved by Response Surface Method. Hence, Policy III is

recommended to mitigate risk in this case study.

5.3 SELECTION OF POST–HARVEST TREATMENT IN THE

FRESH BAMBOO SHOOT SUPPLY CHAIN

Supply chain parties in the bamboo shoot supply chain consist of

growers (harvest), commission agents (collection and post-harvest treatment),

wholesalers (storage), buyer (transportation) and retailers (storage).

Commission agents or traders are key players for making decisions for

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selection of post-harvest technology strategies with respect to the desired

quality level.

5.3.1 Alternative Post-Harvest Treatment Methods

Four post-harvest treatment methods considered are: (1) soaking in

sulfur dioxide solution, (2) blanching, (3) controlled-temperature

transportation, and (4) dipping in vinegar. The details of four treatments are

explained in Table 5.17.

Table 5.17 Post-harvest treatment for bamboo shoot supply chain

TreatmentMethods

Details

Soaking in sulfur dioxide solution

Peeled and Trimmed Bamboo shoots are soaked in a sulfur dioxide solution (10-30% w/v) about 3- 5 seconds, and packed in 10 kilogram plastic bags. Goods are carried to the wholesale market by pick-up (small truck) at ambient temperature.

Blanching Peeled and Trimmed Bamboo shoots are dipped in hot water(80-90 C) for 1 second followed by cool water (0-1 C)

for 3 seconds, and packed in 10 kilogram plastic bags. Goods are carried to the wholesale market by pick-up (small truck) at ambient temperature.

Controlled-temperature during storage and transportation

Peeled and Trimmed Bamboo shoots are soaked in cool water for 5 � 10 minutes and packed in 5 kilogram plastic bags. Each five kilogram bamboo shoot pack is covered by an ice bag and kept in an ice tank(approximately 600 kilograms per tank). Goods are carried to the wholesale market by pick-up (small truck).

Dipping in vinegar Peeled and Trimmed Bamboo is dipped in hot vinegar for 3-5 seconds and packed in 10 kilogram plastic bags. Goods are carried to the wholesale market by pick-up (small truck) at ambient temperature.

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5.3.2 Criteria for Selecting Post-Harvest Treatment

Post-harvest technology has impact on the quality attributes of the

product reaching the customer (Aked 2002). However, when an individual is

willing to purchase fresh bamboo shoots, not only quality issues must be

considered. Economic issues must also be taken in account. From information

obtained through interview and discussion, there are five related criteria

considered including: food safety, whiteness, shelf life, price and peeled

bamboo shoot color (shown in Table 5.18).

Table 5.18 Food safety, whiteness, shelf life, price and peeled bamboo

shoot color

Criteria Explanation

Food Safety Food safety for consumers� health

Whiteness Whiteness at cutting area after trimming

Shelf life Product can last during storage

Price Wholesale price

Peel Appearance Peel colour and shape

The conceptual model of the proposed method for selecting

post-harvest technology is shown in Figure 5.10. From the identified risk, the

specified alternative post-harvest technology methods were chosen to be

performed and then people involved in the chains were interviewed(both

private and government sectors). Post-harvest technological goals were

defined and the hierarchy scheme was constructed. FDM was used to obtain

data from field work for the pairwise comparisons. Using the Centriod

Method to quantify fuzzy numbers associated with the pairwise comparisons,

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values will be transformed to crisp values. Finally by applying the AHP

approach, the optimum post-harvest technology strategy was selected.

Figure 5.10 Conceptual model for selecting post-harvest treatment and

continuous improvement

5.3.3 Generic Hierarchy Structure: Goal, Criteria, and Alternative

Multiple Attribute Decision Making (MADM) technique is applied

in order to make decisions in the presence of multiple and conflicting criteria.

In MADM, AHP recognizes the basic rationality by breaking down a problem

into smaller and smaller constituent parts and then guides the decision maker

Identification of Post-harvest Treatment alternatives Identification of the decision criteria

Construct the hierarchy scheme

Construct Pairwise comparisons using fuzzy Delphi method

Defuzzification

Utilize AHP method to find optimum post-harvest Treatment

Knowledge Base of Post-Harvest Management for Fresh Bamboo

Shoot

Fuzzy Inference System (FIS)

Data obtained from field work through observation, interview and discussion

Risk Value for Sustainability of SC

Implementation of the selected method

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through a series of pairwise comparison judgments to express the relative

strength or intensity of impact of the elements in the hierarchy.

5.3.4 Consolidation of Expert Input: Fuzzy Delphi Method

The value obtained from input factors was vague and ambiguous.

When Decision Maker (DM) considers input criteria to select post-harvest

treatment methods, owing to the imprecise and uncertain pairwise rating of

these factors and a general lack of data for their probabilistic quantification,

fuzzy logic is implemented. The values of the input factors are determined

based on the experience and subjective judgment of experts involved in the

post-harvest chain.

Fuzzy logic is an analytical approach that allows for multiple

membership of sets and different levels of belonging to any one set. The basic

assumption is that no clear boundaries exist between members and non-

members of a set, which has been defined as a collection of distinct things

considered as a whole. The notion of fuzzy logic has been used in a wide

range of topics in agricultural management. However, the notion of fuzzy

logic theory has not been developed in the agricultural supply chain as a

means of assessing this research. We use the people involved in the

agricultural chain in the East of Thailand as a case to demonstrate that fuzzy

set theory can be used as an effective approach to examining post-harvest

technology�s contribution values, particularly when enablers in the chain hold

multiple values with different levels of emphasis. The consolidation of input

factors can be done using FDM. The final consolidated fuzzy numbers will

act as rating for AHP approach to evaluate criteria and technological strategy.

The FDM consists of the following steps (Kaufmann and Gupta,

1988; Shaheen et al 2007):

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Step 1: �n� number of experts provide the estimates of input factors

in the form of triangular fuzzy numbers . Triangular

membership function is shown in equation (5.1). In Delphi technique highly

qualified experts must be interviewed to give their opinions regarding a

specific issue. The minimum number of experts required in Delphi technique

has been reported as 12 persons (Kaufmann and Gupta, 1988). Each expert

may give his/her best estimate on the amount of input factor based on his/her

experience as a triangular fuzzy number. For this purpose, the experts are

asked to provide three estimates of a specific value by determining the

minimum, the most plausible, and the maximum estimate. The estimates are

presented in the form of triangular fuzzy numbers:

(5.1)

Step 2: The estimates are averaged. For each expert, the deviation

from the average is calculated as shown below:

(5.2)

(5.3)

where F_ave = fuzzy average; and (a_ave b_ave c_ave ) = first, second and

third elements of the fuzzy number, respectively.

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Step 3: The deviations in the estimates are sent back to the experts

for revision. Each expert provides a new triangular fuzzy number. Steps 1�3

are repeated until two successive averages become reasonably close based on

the decision maker�s stopping criterion as described in step 4 (Shaheen et al

2007).

Step 4: This is step for defuzzification. It transforms the resulting

fuzzy values from expert�s judgement induced into crisp value. The centre of

area method (Z*) as function shown in (5.4) is utilized for defuzzification

(Zimmermann 2001)

(5.4)

5.3.5 Application of Analytic Hierarchy Process Approach and

Numerical Illustration

Defuzzified value obtained from step 4 is applied for pairwise

comparison in the AHP approach. In pairwise comparison for technology, we

consider how the first technologies being compared can contribute to the

objective over the second technology. Nine point scale used in this study is

shown in Table 5.19.

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Table 5.19 Nine point scale rating

Scale Explanation

1 Both treatment methods contribute equally to the objective

3 Moderate importance, slightly favor one treatment over the other

5 Strong importance, strongly favor one over the other

7 Very strong importance, strongly favor one over the other

9 Extremely importance, the first treatment is extremely relevant to contribute to object than the other

5.3.6 Risk Assessment

For the indentified risk, elementary risk, risk factor, elementary risk

factor shown in Table 5.3, value of severity and possibility quantified through

discussion with experts involved in the bamboo shoot supply chain as shown

in Table 5.20.

Table 5.20 Risk Assessment

Elementary Risk Severity Possibility Risk Value

Chemical Hazard Risk 6 0.70 4.20

Biological Hazard Risk 4 0.40 1.60

Product Appearance Risk 1 0.90 0.90

Over cost risk 3 0.40 1.20

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In Table 5.20, risk value of chemical hazard risk is highest but this

risk directly impacts consumers in the short term. This risk can indirectly

impact traders and wholesalers in the long term. Even though chemical

hazard risk obtained the highest value, most traders or commission agents still

pay attention to mitigate marketing and economic risk. However, if risk to the

consumer is not taken into account, the resulting decision can never lead to a

sustainable supply chain. With respect to sustainability of supply chain in

bamboo shoot supply chain, the Fuzzy Inference System (FIS) was modeled

in order to predict risk value for sustainability. FIS structure is shown in

Figure 5.11.

Figure 5.11 Relationship between the risk input factors, risk

In Figure 5.11, sustainability risk value is the highest risk among

consumers, wholesalers, and traders (commission agents) because the long

term risk from different parties in the chain can affect each other. Any

operation used must consider win-win situations for the entire supply chain in

the long term. Max operation was used in the fuzzy inference system in order

to quantify elementary factor to sustainable risk. So, from this point of view,

we found that sustainable risk was highest due to chemical hazard risk. Hence

selection of post-harvest treatment methods in order to mitigate this risk is

required.

Risk event magnitude for Sustainability

Consequence of risk

Fix and Operation Complexity li ti

Awareness of consumers

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5.3.7 The Selection of Post-Harvest Treatment

An algorithm is presented based on the proposed approach for the

selection of the post-harvest technology to be performed specific to the

bamboo shoot supply chain in The East of Thailand. It is assumed that the

availability of each post-harvest technology is constant. The algorithm is

presented in five steps:

Step 1:

The goal considered for achieving the success in the post-harvest

bamboo shoot chain is to find the most appropriate method with respect to

five criteria given in Table 5.21.

Step 2:

For the information gathered in step 1, the AHP is applied. AHP is

a decision-aid that can provide the decision maker (DM) with relevant

information to assist the DM in choosing the most plausible alternative. The

hierarchy scheme for the proposed AHP is shown in Figure 5.12.

Figure 5.12 The hierarchy scheme

WhitenessSafety Appearance

SO2 Blanching Vinegar Controlled

Shelf Life Price

The most appropriate post-harvest-technology

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Step 3: Representatives from commission agents in Prachinburi,

wholesalers of the bamboo shoot business chains located at Simummuang

Rangsit and Taladthai Rangsit markets, faculty members and officials from

government agencies were considered the as experts to estimate the

importance of decision criteria. Worst cases, general cases, and best cases are

assumed to make experts estimated minimum important value (a), most

plausible important value (b), and maximum important value (c) for each

pairwise comparison from five criteria. The eigenvectors derived in this step

reflect the weights associated for each criterion. These details are presented

in Table 5.21.

Table 5.21 Decision criteria: pairwise comparison

Criteria Weight Priority

Food Safety 0.536 1

Whiteness 0.208 2

shelf life 0.163 3

Price 0.057 4

Peel Appearance 0.035 5

Step 4: The comparison of post-harvest technologies under each

criterion was carried out. However, in the case of technological comparison

we obtained crisp values. The eigenvectors derived from this step are

presented in Table 5.22 along with weights obtained for each criterion in

step 4.

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Table 5.22 Post-harvest Treatment comparison with respect to each

criterion and overall ranking

CriteriaFoodSafety

WhitenessShelfLife

Price Peel Weight Priority

Priority 0.536 0.206 0.163 0.057 0.035

Using Sulfur 0.103 0.472 0.418 0.446 0.449 0.2617 2

Blanching 0.430 0.384 0.361 0.386 0.380 0.4037 1

Dipping in vinegar 0.159 0.089 0.082 0.105 0.069 0.1253 4

Controlled-temp. 0.308 0.056 0.140 0.063 0.102 0.2066 3

The responses received from commission agents, wholesalers and

experts from academia are checked for consistency. These details are

presented below.

In order to estimate relative weigh of criteria in pairwise

comparative matrix A, priority of criteria is compared by computing

eigenvalues and eigenvectors as

(5.5)

where w is eigenvector of matrix A; max is largest eigenvalue of matrix A.

Consistency of matrix is achieved by examining reliability of

judgement in pairwise comparison. Consistency Ratio (CR) and Consistency

Index (CI) are defined as

(5.6)

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(5.7)

where n is number being comparing in the matrix, and RI is random

consistency index. RI depends on number of criteria being compared as

shown in Table 5.23.

Table 5.23 Random Consistency Index

Number of criteria (n) 3 4 5 6

Random Consistency

Index (RI) 0.58 0.90 1.12 1.24

As shown in Table 5.22, blanching treatment is the most significant

method with an overall priority of 0.4037.

As shown in Table 5.23, for the five criteria pairwise comparison,

RI is 1.12 and on another hand, from the four alternative technological

pairwise comparisons RI is 0.09. From equation (5.5), (5.6), (5.7) and RI

from table 5.23, we determined CR and is shown in Table 5.24.

Table 5.24 Consistency Ratio of pairwise comparison

Respect to n CI RI CR

5 Criteria Safety, Quality and Economics

5 5.32 0.64 1.12 0.058

4 treatments Safety 4 4.06 0.020 0.90 0.022

4 treatments Whiteness 4 4.25 0.086 0.90 0.095

4 treatments shelf life 4 4.21 0.069 0.90 0.077

4 treatments Price 4 4.21 0.071 0.90 0.079

4 treatments Peel Color 4 4.23 0.079 0.90 0.088

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Since all CR is less than 0.1, so the pairwise comparison is found to

be consistent.

5.4 CONCLUSION

In the case study of the bamboo shoot supply chain in Thailand,

risk issues and risk factors were indentified through supply chain risk

assessment guideline reported by World Bank and Jaffee et al (2010). Experts

from academia, agribusiness business and government sectors were

interviewed. Data obtained from experts in the agribusiness sector was

ambiguous. Chemical hazard risk gives the highest risk value.

Fuzzy logic and system dynamics approach are applied to develop

an integrated supply chain management process to deal with the complex

dynamic behavior and uncertain nature of supply chain risks. It was illustrated

that the uncertain and imprecise nature of risks could be better approached by

integrating fuzzy logic into the proposed SD-based model. The applicability

and performance of the proposed method in the risk management process was

evaluated by bamboo shoot production and distribution in Thailand. The

impact of this risk on revenue was well quantified. The effectiveness of

alternative response scenarios may be employed to mitigate the negative

impacts of the risk. Farmers� income with respect to alternative risk

management policies for sustainable growth was analyzed using the proposed

fuzzy SD approach. Design of experiment was used for simulate the proposed

SD model.

The output from the SD model was applied through response

surface methodology to obtain the optimum condition that maximizes the

profit.

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The study also investigates the key factors for selection of

post-harvest treatment by combining FDM and AHP. From four possible

post-harvest treatment alternatives, pairwise comparisons of five evaluative

criteria influencing post-harvest technology selection are analyzed through

expert�s opinions. FDM was used to quantify those fuzzy to crisp values

before evaluation through AHP. FDM and AHP were used to integrate

expert�s opinions to obtain the significance of various criteria. The results

from experts of different fields were compared and analyzed. Finally, it was

found that blanching method is the most recommended treatment method.

Integration of FDM and AHP was done for selecting future post-harvest

technology.

These proposed integrated approaches are useful for production and

distribution of horticultural products with uncertainly and imperfect

information.

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