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Posts from the ‘Thailand’ Category

แนวทางการพัฒนาระบบโลจิสติกส์ของประเทศไทย


ท่านคณบดี ดร.พิสิฐ ลี้อาธรรม ได้สอบถาม (ทาง Line group ของคณะฯ) ผมว่า มีแนวทางในการพัฒนาระบบโลจิสติกส์ของไทยอย่างไรบ้าง?

ผมตอบไปดังนี้ครับ

Dean Pisit: For logistic discipline followers, Thailand should adopt a KPI to reduce our cost and time to the level of Singapore.

Ajarn Champ:
I think the Ministry of Industry is now trying to implement the World Bank approach of Logistics Performance Indicator (LPI) in Thailand.
Sadly the main target they aim is only to reduce logistics cost, which is only one side of more than 10 aspects in logistics decision making.

Dean Pisit: Pls show me so i could find a way to bring it forward.

Pairach: yes, sir
Screen Shot 2558-04-09 at 11.14.47 AM
Pairach: This is the recent finding of the global Logistics performance at the macro level
Pairach: The logistics performance (LPI) is the weighted average of the country scores on the six key dimensions:
1) Efficiency of the clearance process (i.e., speed, simplicity and predictability of formalities) by border control agencies, including customs;

2) Quality of trade and transport related infrastructure (e.g., ports, railroads, roads, information technology);

3) Ease of arranging competitively priced shipments;

4) Competence and quality of logistics services (e.g., transport operators, customs brokers);

5) Ability to track and trace consignments;

6) Timeliness of shipments in reaching destination within the scheduled or expected delivery time.
Pairach: http://lpi.worldbank.org/international/global

Screen Shot 2558-04-09 at 11.16.29 AM

Pairach: The performance of Thailand since 2007 – 2014 has no significant improvement krub
Pairach: Pairach Still lag behind not only Singapore but does Malaysia
Pairach: Thailand vs Singapore vs Malyasia vs Vietnam in 2014

Screen Shot 2558-04-09 at 11.18.10 AM
Pairach: Clearly show that we are the number 3 in ASEAN.
Pairach: Vietnam is only their way to beat us, similar to what Malaysia did.
Screen Shot 2558-04-09 at 11.19.29 AM
Pairach: Singapore is the best in infrastructure at the global level. In ASEAN Singapore is the best in all aspects. Comparing Thaialnd vs Malaysia, Thailand is slightly better than Malaysia only in “Timeliness”. Big issue for Thailand is custom clearance and logistics competence (Human resource issue). Ability to manage and handle logistics properly is what Thailand missing.
Screen Shot 2558-04-09 at 11.26.18 AM
Pairach: Now Thailand ranked NO 35
Pairach: To move up the the top tier, National data tools and Green logistics are important.

Dean Pisit: We should use this sort of ranking to challenge the Ministry and Salary could be adjusted accordingly.

Pairach: Could not agree more
Pairach: I believe they know but it’s very difficult to meet this KPI.
Pairach: Reducing Average logistics costs is easier, but not the ultimate answer.

Dean Pisit:  If proper incentive is put in place, there could be guided to work hard.

Pairach: To the best of my knowledge, the ministry of Industry still struggle to measure the impacts of their activities and project on the logistics cost at the national level.
Pairach: Agree on using incentive to push the private sector to develop their logistics competence, not just only the cost.
Pairach: Perhaps could be integrated in the agency like BOI.
Pairach: At the international level, one recent finding by WEF show that “reducing the international supply chain barriers” performs better than “reducing tariff” 7 times in term of GDP growth.
Dean Pisit:  Let international rating be the standard.
Screen Shot 2558-04-09 at 11.41.46 AM
Pairach: Here are the supply chain barriers we should reduce across the border and port krub.
Pairach: The following is the comparing the effects of “Reducing supply chain barriers” vs “reducing tariffs” on GDP growth krub.
Screen Shot 2558-04-09 at 11.42.59 AM
Pairach: However the effects could vary in different regions.

Screen Shot 2558-04-09 at 11.44.09 AM
Pairach: With simulation, ASEAN could gain 12% Export and 18% import growth by reducing supply chain barrier. Also we hardly compete to Singapore in terms of logistics cost because we have different logistics system support ting different product type. We are export more agricultural products much more than Singapore. However, we can develop value creation process in the supply chain, for example transformed agricultural products such as fresh longan into not just dried longan but things like snack or drink or even medical proucts like Longanoid cream (for joint problems). That’s my opinions.

แหล่งข้อมูล ประชาคมเศรษฐกิจอาเซียน AEC


แหล่งข้อมูลสำหรับ AEC หรือ ASEAN Economic Community

Official website

Thailand

Malaysia

Using R to Analyse Tourism Data – Part 1: Visualising Tourist Profile


Tourism is an important sector in the global economy. In many countries, tourism is the main source of revenue, Thailand is one of them. However, tourism sector is a fast moving sector. It is very sensitive to various factors and also vulnerable. The tourism markets for each destination (country) are also very diverse. Tourism data are available and updated frequently. One of the most important report of national tourism statistics; number of tourist arrivals from each country of origin, their average length of stay and total receipt or expenditure. These tourism statistics are important but often reported separately due to the limitation of software used by analysts.

The following graph represents profile of international tourists in Thailand in 2005.

The picture above was produced in R with package ‘ggplot2’ using the code below.


# Step 1: Import data into R
exp05 <- read.csv("http://dl.dropbox.com/u/46344142/thai_tour_2005.csv", head = T)
# Step 2: Load 'ggplot2' package for plotting elegent data visualisation
library(ggplot2)
# Step 3: Specify x and y axis, label, size of the bubbles and colour of the region
exp <- ggplot(exp05, aes(x=number, y=length, label=country, size=receipt, colour = region))
# Step 4: Create a plot and add texts to x and y axis
exp + geom_point() + geom_text(hjust=0.7, vjust=2) + labs(x = "Number of Tourist Arrivals", y = "Length of Stay (days)") + scale_area("Receipt (M. USD)") + scale_colour_hue("Region")

Measuring Emergency Relief Performance of Thailand Floods in 2011


Background

Last year Thailand faced the worst floods in their history. The World Bank (2011) reported that the 2011 Thailand floods were “The biggest damages and losses were in the manufacturing sector, with a total of THB 1,007 Bn (USD 32 Bn approximately)”. The tourism and agricultural sector were also affected and losses approximately THB 95 Bn (USD 3 Bn) and THB 40 Bn (USD 1.3 Bn) respectively (The World bank, 2011).

Therefore, this study aims to evaluate the performance of organisations that took actions in emergency relief for this disaster. We measure performance based on their preparedness for such disaster as well as how they responded to the events. Data collected via GoogleDoc from 382 respondents (victims) were (A) explored and the (B) used to fit with the conceptual model of performance measurement for emergency relief logistics.

A. Preliminary Findings

  • Descriptive_GoogleDoc (in Thai)
    A chart below shows the proportion of organisations, which samples have got helps.
    Highest percentage is unknown organisation, followed by military agencies.
  • Interesting Info-graphic obtained using R

What we can get from R

Moving away from the basic barplot instantly provided by Google Doc, we can do a better job to visualise what  people rate the performance of each organisation in terms of preparedness and response to the floods. An we can do so via R. Followings are what I produced in R.

1. Level Preparedness 

# Boxplot preparedness performance

k <- ggplot(flood, aes(factor(org), aPRE))

k + geom_jitter(aes(colour = org), size = 4) + opts(legend.title = theme_text(size = 20, face = "bold"), legend.text = theme_text(size = 10)) + opts(title = "Level of Perceived Preparedness of each Organisation") + opts(plot.title = theme_text(size = 15, face="bold", colour = "blue"))

2. Level of Responses

# Boxplot response perforance

c <- ggplot(flood, aes(factor(org), aRES))

c + geom_jitter(aes(colour = org), size = 4) + opts(legend.title = theme_text(size = 20, face = "bold"), legend.text = theme_text(size = 10)) + opts(title = "Level of Perceived Response of each Organisation") + opts(plot.title = theme_text(size = 15, face="bold", colour = "blue"))
<pre>

B. Theoretical Output: BAM2012 Paper

Title: Developing Measurement of Emergency Relief Logistics and Operations Performance: An Empirical Study of Thailand Floods in 2011

Summary

Albeit emergency relief logistics is an emerging field in operations, logistics and supply chain management, the development of performance measurement is still limited. Although one of the objectives of emergency relief logistics is to satisfy customers (victims in the disaster) the development of performance measurement based on victim’s perspective is limited. Then this study propose a measurement of emergency relief logistics performance and tested with an empirical data from the Thailand floods in 2011. We fit the propose measurement model with the data of 382 respondents using Confirmatory Factor Analysis with Mplus version 6 and R version 2.14.1. The result shows that the model is fit with the data. It was found that response had the highest contribution to the total performance, followed by preparedness and recovery respectively. The result also shows that information, operations and evacuation have different contribution to the performance of each stage.

Keywords: Humanitarian logistics, Emergency relief, disaster, floods, Thailand

Download: pdf

R code used in the paper

</pre>
library(lavaan)
 flood1.cfa <-'

PRE =~ x411 + x412 + x413 + x414 + x415 + x416
 + x421 + x422 + x423 + x424 + x425
 + x431 + x432 + x433 + x434

RES =~ x511 + x512 + x513 + x514 + x515 + x516
 + x521 + x522 + x523 + x524 + x525 + x526 + x527
 + x531 + x532 + x533 + x534 + x536

REC =~ x611 + x612 + x613 + x614 + x615 + x616 + x617
 + x621 + x622 + x623 + x624 + x625 + x626 + x627
 + x631 + x632 + x633 + x634 + x636

PEF =~ PRE + RES + REC

'
 fitFlood1 <- cfa(flood1.cfa, data=flooddata)
 summary(fitFlood1, standardized=TRUE, fit.measures=TRUE)
<pre>

Tourism Associations and Agencies in Thailand – รายชื่อสมาคมและหน่วยงานด้านการท่องเที่ยวในประเทศไทย


หน่วยงานภาครัฐ

  1. กรมการท่องเที่ยว (Department of Tourism)
  2. กระทรวงการท่องเที่ยว และ กีฬา (Ministry of Tourism and Sports)
  3. การท่องเที่ยวแห่งประเทศไทย (Tourism Authority of Thailand)

ระดับประเทศ

  1. สภาอตสาหกรรมท่องเที่ยวแห่งประเทศไทย (Thailand Tourism Council)
  2. สมาคมโรงแรมไทย (Thai Hotel Association)
  3. สมาคมสปาไทย (Thai Spa Association)

ระดับภาค

  1. สมาคมโรงแรมภาคอีสาน

ระดับจังหวัด

  1. เชียงใหม่
    สมาคมธุรกิจท่องเที่ยวจังหวัดเชียงใหม่ (Chiang Mai Tourism Business Association)
    สมาคมมัคคุเทศน์ เชียงใหม่ (Chiang Mai Guide Association) 
  2. สมาคมธุรกิจท่องเที่ยวจังหวัดภูเก็ต (Phuket Tourist Association)
  3. สมาคมธุรกิจการท่องเที่ยว จังหวัดพิษณุโลก
  4. สมาคมท่องเที่ยวเกาะสมุย (Tourism Association of Koh Samui)
  5. สมาคมการท่องเที่ยวและโรงแรมจังหวัดตรัง (Trang Tourism and Hotel Association)
  6. ศูนย์ข้อมูลการท่องเที่ยวจังหวัดนครศรีธรรมราช
  7. สมาคมสมาพันธ์ธุรกิจการท่องเที่ยว จังหวัดสงขลา (Tourists Business Federation of Songkhla)
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