K-COMMODITIES 2.0 KEMENTERIAN PERUSAHAAN...
Transcript of K-COMMODITIES 2.0 KEMENTERIAN PERUSAHAAN...
K-Commodities Fasa 2 High-Level Document
K-COMMODITIES 2.0
KEMENTERIAN PERUSAHAAN PERLADANGAN
DAN KOMODITI MALAYSIA
HIGH-LEVEL DOCUMENT
USER AND SYSTEM REQUIREMENT STUDY
Version 1.0
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K-Commodities Fasa 2 High-Level Document
DOCUMENT INFORMATION
Project Name: KCommodities Fasa 2
Project Manager:
PSK :TMK :KRIM :
En Wan Ibrahim bin Wan AbasEn Jaafar bin JamaanYM Raja Iskandar Shah
Document Title:
HighLevel Document
Document Version:
1.0 Version Date: 29. May. 2009
Prepared by: Puan Syuhada binti Ab Kadir.
Date Prepared: 29. May. 2009
Checked by: YM Raja Iskandar Shah Checked Date: 29. May. 2009
Comment:
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DISTRIBUTION LIST
From Date Tel / Fax
Puan Syuhada binti Ab Kadir 29. May. 2009 H/P : 0199697372
To Action Date Tel / Fax
En Wan Ibrahim bin Wan AbasSUB (PSK)
Approve Tel: 0388803435 ext 3424Fax: 0388803441
En. Jaafar JamaanP (TMK)
Verify Tel: 0388803423 ext 3416Fax: 0388878334
Peter DennisKPSU (PSK) III
Check Tel: 0388803435 ext 8340Fax: 0388803441
Arnold Denis PautPP (TMK)
Check Tel: 0388803423 ext 3419Fax: 0388878334
REVISION HISTORY
No Version By Description Date
1 1.0 Puan Syuhada binti Ab Kadir
Initial Draft 05/29/09
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ABBREVIATIONS
GET Bahagian Kemajuan Industri Getah
KPPK Kementerian Perusahaan Perladangan & Komoditi
KPSU Ketua Penolong Setiausaha
KRIM Krim Network Sdn. Bhd.
MCB / LKM Malaysian Cocoa Board / Lembaga Koko Malaysia
MPB / LLM Malaysian Pepper Board / Lembaga Lada Malaysia
MPOB / LMSM Malaysian Palm Oil Board / Lembaga Minyak Sawit Malaysia
MRB / LGM Malaysian Rubber Board / Lembaga Getah Malaysia
MSLS Bahagian Minyak Sayuran, Lelemak dan Sago
MTIB / LPKM Malaysian Timber Industry Board / Lembaga Perindustrian Kayu
Malaysia
NTB / LTN National Tobacco Board / Lembaga Tembakau Negara
P Pengurus
PIMI Bahagian Penyelidikan Inovasi dan Modal Insan
PP Penolong Pengurus
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Table of Contents
1.INTRODUCTION......................................................................................11.1.Project Description........................................................................................................11.2.Project Objectives .........................................................................................................21.3.Project Scope................................................................................................................21.4.Flow of Document.........................................................................................................3
2.STUDY OF LAPORAN BRAINSTORMING PEMBANGUNAN SISTEM K-COMMODITIES.....................................................................................42.1.Background Information ...............................................................................................42.2.Data and Information Required and Availability............................................................42.3.System Modules to be Developed.................................................................................82.4.Issues That Need to be Overcome...............................................................................11
3.STUDY OF PHASE 1 OF THE K-COMMODITIES SYSTEM.............................17
4.QUESTIONNAIRE AND WORKSHOP ON THE REQUIREMENTS OF KPPK AND ITS SIX AGENCIES..............................................................................234.1.Agency Data................................................................................................................24
4.1.1.Malaysian Palm Oil Board (MPOB)..........................................................................244.1.2.Malaysian Rubber Board (MRB)..............................................................................314.1.3.Malaysian Timber Industries Board (MTIB) ............................................................384.1.4.National Tobacco Board (NTB) ..............................................................................464.1.5.Malaysian Cocoa Board (MCB)................................................................................534.1.6.Malaysian Pepper Board (MPB)...............................................................................60
5.PROPOSED PHASE 2 IMPLEMENTATION STRATEGY INCLUDING CURRENT AND FUTURE REQUIREMENTS.............................................................675.1.Organisational Objectives............................................................................................685.2.Dimensions to the Dataset..........................................................................................695.3.Project Management Life-Cycle...................................................................................71
5.3.1.Project Evaluation..................................................................................................725.3.2.Project Initiation.....................................................................................................765.3.3.Project Planning.....................................................................................................765.3.4.Project Execution...................................................................................................775.3.5.Project Closure.......................................................................................................77
5.4.K-Commodities Information Framework......................................................................785.5.Information Sources ...................................................................................................805.6.Implementation Strategy Roadmap.............................................................................83
6.HIGH LEVEL DOCUMENT OVERVIEW OF THE K-COMMODITIES SYSTEM.....876.1.Architecture.................................................................................................................876.2.Modules / Functions.....................................................................................................896.3.Use Case ....................................................................................................................93
6.3.1.Types of Users........................................................................................................936.3.2.Use Case Diagram..................................................................................................93
6.4.Flowchart.....................................................................................................................946.4.1.Type of User : USER...............................................................................................94
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6.4.2.Type of User : ENTITY USER...................................................................................966.4.3.Type of User : EXECUTIVE USER.............................................................................976.4.4.Type of User : SYSTEM ADMINISTRATOR................................................................986.4.5.Type of User : SECTION ADMINISTRATOR...............................................................99
APPENDICES :
A SYSTEM KCOMMODITIES PHASE 1 1
B INFORMATION PUBLISHED IN THE AGENCIES WEBSITE
B1 MALAYSIAN PALM OIL BOARD (MPOB) 7
B2 MALAYSIAN RUBBER BOARD (MRB) 77
B3 MALAYSIAN TIMBER INDUSTRY BOARD (MTIB) 91
B4 NATIONAL TOBACCO BOARD (NTB) 113
B5 NATIONAL COCOA BOARD (NCB) 117
B6 MALAYSIAN PEPPER BOARD (MPB) 126
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1. INTRODUCTION
This document provides a high level overview of the KCommodities system and the
result of the user and system requirement study conducted. It is intended to capture
and convey the overall framework which will form the core building blocks of the
system.
1.1. Project Description
This project involves the study, development, supply, installation, testing,
implementation and provision of training for Phase 2 of the KCommodities (Knowledge
Commodities) system at Kementerian Perusahaan Perladangan dan Komoditi (KPPK).
This project will be done over the period 1 March 2009 till 28 February 2010.
The project will directly involve Bahagian Perancangan Strategik dan Korporat (PSK)
as the principal owner, Bahagian Teknologi Maklumat dan Komunikasi (TMK) as
project facilitator, and the six agencies under KPPK, namely, Malaysian Palm Oil
Board (MPOB / LMSM), Malaysian Rubber Board (MRB / LGM), Malaysian Cocoa
Board (MCB / LKM), Malaysian Pepper Board (MPB / LLM), Malaysian Tobacco Board
(MTB / LTN), and Malaysian Timber Industry Board (MTIB / LPKM).
KCommodities is a knowledge system for commodities which includes palm oil,
rubber, cocoa, pepper, tobacco, and timber that are currently under KPPK. It is
intended to encourage the use and sharing of data and information to enhance
knowledge on these commodities. It is to consist of two types of module categories:
1. Information System : which is to gather, process and report quantitative data.
2. Knowledge Base : which is to gather and store textual information.
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The completed Phase 1 of KCommodities consisted of the development of the
Information System modules and the manual online updates of 6 types of data from the
six agencies.
1.2. Project Objectives
• To establish a central system for KPPK and its agencies that will gather uptodate
strategic information of the commodities from various data sources
• To provide a centralized tool for KPPK and its agencies to perform analysis and
reporting to facilitate decision making processes
• To facilitate a standardized process for transferring commodity data from the
various data sources to KPPK through an integration between the data sources to
a central commodity data warehouse
1.3. Project Scope
The scope of this Phase 2 KCommodities project is to upgrade Phase 1 of K
Commodities which involves the study, development, supply, installation, testing,
implementation and provision of training for Phase 2 of the KCommodities. It will
consist of four primary activities:
1. User and system requirement study for the current and future needs of KPPK
2. Development of the system according to the requirements set out
3. Supply of one unit of server for reporting
4. Supply of a business intelligence software
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1.4. Flow of Document
This document is structured according to the following outline :
1. Introductory brief to Phase 2 of the KCommodities project (this chapter)
2. The study of the Laporan Brainstorming Pembangunan Sistem KCommodities
document
3. The study of the Phase 1 KCommodities system (structure and screenshots
attached in Appendix A)
4. Questionnaire and workshop on the requirements from KPPK and the six
agencies (screenshot of information published on agency website attached in
Appendix B)
5. Proposed Phase 2 implementation strategy including current and future system
enhancements
6. High level document overview of the KCommodities system
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2. STUDY OF LAPORAN BRAINSTORMING PEMBANGUNAN SISTEM KCOMMODITIES
This part of the study involves a review and tabulation of the main data and functional
requirements highlighted in the Laporan Brainstorming Pembangunan Sistem
Knowledge Commodities.
2.1. Background Information
A brainstorming program was held on 11 – 13 August 2006 which was attended by 28
participants from KPPK and MPOB, LKM, LTN, MTIB and LGM agencies. The
objective of the brainstorming program was to:
1. Identify data and information requirements from the KPPK Divisions
2. Identify whether data and information required are available from the Agencies'
system
3. Identify the system modules that are to be developed
4. Identify issues that need to be overcome
5. Develop an Action Plan for the development of KCommodities
2.2. Data and Information Required and Availability
34 sets of data were identified as required and were available from the agencies. The
list of 34 sets of data have been included as part of the project scope of work for Phase
2 of the KCommodities project. The following table lists the 34 sets of data and the
divisions requesting for those data.
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No. Data PSK PAB MSLS
KTK IKL GET PPK PIMI
1 Keluasan (ha) Y Y Y Y Y
2 Pengeluaran Y Y Y Y Y
3 Harga (tempatan) Y Y Y Y Y
4 Harga (dunia) Y Y Y Y Y
5 Eksport Y Y Y Y Y Y
6 Import Y Y Y Y Y Y
7 Guna tenaga(tempatan)
Y Y Y Y
8 Guna tenaga(asing)
Y Y Y Y
9 Produktiviti(Hasil/ha/thn)
Y Y
10 Tahap PenggunaanTeknologi(mekanisasi/ otomasi)
Y Y Y
11 Bilangan usahawanterlibat
Y Y Y Y
12 Bilangan R&D dihasilkan ataudikomersilkan
Y Y Y Y
13 Bilangan lesendikeluarkan
Y Y
14 Data pesaing(negara lain)
Y Y
15 Data pesaing(komoditi/ produk lain)
Y Y
16 MoU
17 Projek RMK 8 & 9 Y Y Y Y
18 Jawapan Parlimen Y Y Y Y
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No. Data PSK PAB MSLS
KTK IKL GET PPK PIMI
19 Pendapatan PekebunKecil
Y
20 Bilangan Kilang Y Y Y Y
21 Dasar / Polisi Y
22 Tariff Y Y
23 Non tariff barrier Y
24 Unjuran penggunaan Y Y Y Y
25 Maklumat negaralain (profil)
Y Y
26 Maklumat AP Y
27 Kuota pengeluaran Y
28 Isuisu semasaberkaitan industri
Y
29 Curing barn Y
30 Tanaman integrasi Y
31 Isuisu perjawatanagensi
32 Direktori / senaraipelesenan
Y
33 Laporanlaporankewangan / tahunanagensi
Y Y Y Y
34 Lokasi kawasanpengeluaran dalamnegara
Y Y Y Y Y
There are a few common data characteristics that were highlighted by the divisions:
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1. Type of information whether quantitative data or textual information or directory
listing.
2. Time series whether in day, week, month, quarter, and year.
3. Entity categorisation such as government / private, or estates / schemes /
smallholders.
4. Projects categorisation for RMK8 and RMK9.
5. Item categorisation such as upstream, midstream and downstream activities,
products and value add.
6. Geographical categorisation according to country, region, state, and district.
7. Measure value whether in currency, weightage or size.
Additional data that were requested by the divisions that were not included in the
above list of 34 datasets were:
1. Value added products and the values by PAB and MSLS (to be added to #2
Pengeluaran (production) above where the items are to be categorised
according to upstream, midstream, downstream sectoral value chain).
2. Incentives provided and the values by MSLS and PIMI (to be added to #29
Curing Barn above as it shares the same sectoral value chain characteristics).
3. Services provided by the agencies by KTK (to be added to #29 Curing Barn
above as it shares the same sectoral value chain characteristics).
4. Consumption by KTK and IKL (to be added to #24 Unjuran Penggunaan
(forecast consumption) above so that comparative analysis can be made
between forecast and actual numbers).
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5. Materials used in production by PSK, IKL and PIMI (to be added as a new
dataset on production cost).
6. Currency exchange rate by IKL (to be added to #25 to form part of the
quantitative statistics of other nations).
7. List of R&D organisation, activities, produced, patented and commercialised by
PIMI (to be added to #12 to form part of textual information on R&D activities).
8. Training institutions, courses, trainers and participation by PIMI (to be added to
#29 Curing Barn above as it shares the same sectoral value chain
characteristics).
The data requirements is further elaborated in the following chapter where this
consulting team had questionnaires, interviews and workshop with the six agencies.
2.3. System Modules to be Developed
The 3 key objectives forms the basic foundation for the development of the modules for
the KCommodities system. The 3 objectives are the same as the project objectives
above:
1. To establish a central system for KPPK and its agencies that will gather uptodate
strategic information of the commodities from various data sources
2. To provide a centralized tool for KPPK and its agencies to perform analysis and
reporting to facilitate decision making processes
3. To facilitate a standardized process for transferring commodity data from the
various data sources to KPPK through an integration between the data sources to
a central commodity data warehouse
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The individual system modules were not summarised and elaborated. The following
table lists suggestions made from the supporting presentation slides from the
respective divisions and a column for this consulting team to propose functional
requirements that can address these suggestions. (Note that the suggestions were
originally listed in Bahasa Malaysia and is now translated for this document).
No. Suggestion Proposed Functional Requirements
1 MSLS Division : Database for a complete statistics
• Centralise database repository to store quantitative and textual information.◦ Data Entry form to gather quantitative
data.◦ File Upload form to gather textual
information. ◦ File Import form to gather quantitative
data in spreadsheet format.◦ HTML Select process to gather
statistics data which are published on website.
◦ Dataset menu to organise the data / information.
◦ Dataset Time dimension to organise the data according to time.
◦ Report view to display the quantitative data.
◦ Report Time Selection feature to select the time period to be displayed.
◦ Report Print feature to print the selected quantitative data.
◦ Report Export feature to produce the quantitative data in spreadsheet and document format.
◦ Document view to display the textual information.
◦ Document Download feature to download or print the selected textual information in document format.
2 MSLS Division : • Centralise database repository to store
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No. Suggestion Proposed Functional Requirements
Direct Access to MPOB / MPOC database for additional information (Export, Import, Productivity, Production, etc)
quantitative and textual information.◦ File Import form to gather quantitative
data in spreadsheet format.◦ Data Load process to gather
quantitative data from agencies' database.
◦ HTML Select process to gather statistics data which are published on website.
◦ Dataset menu to organise the data / information.
3 MSLS Division :Users can make comparison between commodities and competing commodities (domestic and foreign)
• Centralise database repository to store quantitative and textual information.◦ Data Entry form to gather quantitative
data.◦ File Import form to gather quantitative
data in spreadsheet format.◦ HTML Select process to gather data
which are published on website.◦ Dataset Manager to organise the
data / information.• Centralised analysis tool to analyse
quantitative information.◦ Query view to select and display
information from different commodity datasets.
◦ Query Export feature to produce the quantitative data in spreadsheet and document format.
4 GET Division :Have a database for the purpose of statistics
• Same as Suggestion #1 above
5 GET Division :Data on the KPPK website are the same as the data on the agency website
• Same as Suggestion #2 above
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No. Suggestion Proposed Functional Requirements
6 GET Division :User can obtain comparative information between other commodities
• Same as suggestion #3 above
7 PIMI Division:Other than the agencies under KPPK, information may also be obtained from other agencies such as DoS, EPU, MIDA and Immigration Dept.
• Same as suggestion #3 above (subject to approval from the other agencies)
8 PIMI Division:The KCommodities system is hoped to be able to assist in creating a centralised database that is comprehensive in relation to innovation research and human capital within the plantation and commodity sector.
• Same as Suggestion #1 above
2.4. Issues That Need to be Overcome
A number of issues were highlighted during the brainstorming program. The following
table lists the issues highlighted and a column for this consulting team to propose
functional requirements that can address those issues. (Note that the issues were
originally listed in Bahasa Malaysia and is now translated for this document).
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No. Issue Proposed Functional Requirements
1 Most information required are received through phone, letters, fax and email.
• Centralise database repository to store quantitative and textual information.◦ Data Entry form to gather quantitative
data.◦ File Upload form to gather textual
information. ◦ File Import form to gather quantitative
data in spreadsheet format.◦ Dataset Manager to organise the
data / information.◦ Report view to display the quantitative
data.◦ Document view to display the textual
information.
2 There are requests from external organisations to obtain information from several years in the past, for example prices for the past 30 years, which cannot be fulfilled as the information is not stored.
• Centralise database repository to store quantitative and textual information.◦ Data Entry form to gather quantitative
data.◦ File Upload form to gather textual
information. ◦ File Import form to gather quantitative
data in spreadsheet format.◦ Dataset menu to organise the data /
information.◦ Dataset Time dimension to organise
the data according to time.◦ Report view to display the quantitative
data.◦ Report Time Selection feature to
select the time period to be displayed.◦ Report Print feature to print the
selected quantitative data.◦ Report Export feature to produce the
quantitative data in spreadsheet and document format.
◦ Document view to display the textual information.
◦ Document Download feature to download or print the selected textual information in document format.
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No. Issue Proposed Functional Requirements
3 Requests from KPPK to the agencies are often repetitive and duplicating. Example of a duplicating incidence information that is requested by PSK and another division requesting the same information. Example of repetitive incidence is information that have been requested before.
• Standardise process of storing quantitative and textual information.◦ User manager to allow sharing of
information between the divisions and agencies.
◦ Dataset manager to organise the information like in the annual Statistics on Commodities handbook so that it is easy to find.
◦ Log Statistics to track access and regular updating of information.
◦ Log Statistics EMail Alert function to remind the officer in charge if the information not updated within the time limit.
4 Requirement so that information that is obtained can zoomin (example from state to district and mukim)
• Centralise database repository to store quantitative and textual information.◦ Dataset Geography dimension to
organise the data according to geography such as country, region, state, district, parliament and mukim.
◦ Dataset Geography Consolidate feature to calculate the consolidated totals based on the detailed data.
◦ Report DrillDown feature to zoomin from summary totals to detailed data.
5 Country Brief information are available from PAB but not updated.
• Standardise process of storing quantitative and textual information.◦ User manager to allow sharing of
information between the divisions and agencies.
◦ Log Statistics to track access and regular updating of information.
◦ Log Statistics EMail Alert function to remind the officer in charge if the information not updated within the time limit.
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No. Issue Proposed Functional Requirements
◦ Data Entry form to gather quantitative key national statistics data.
◦ File Upload form to gather textual information.
◦ HTML Select process to gather key national statistics data which are published on website (eg World Bank)
◦ Dataset Geography dimension to organise the data / information according to continent and country.
◦ Report view to display the quantitative data.
◦ Document view to display the textual information.
6 Information from external organisations such as IPC, DoS, EPU, MIDA, Immigration are required.
• Centralise database repository to store quantitative and textual information.◦ User manager to allow sharing of
information between the divisions and agencies and external organisations.
◦ Log Statistics to track access and regular updating of information.
◦ Data Entry form to gather quantitative data.
◦ File Upload form to gather textual information.
◦ File Import form to gather quantitative data in spreadsheet format.
◦ HTML Select process to gather data which are published on website.
◦ Dataset Manager to organise the data / information.
◦ Report view to display the quantitative data.
◦ Document view to display the textual information.
7 Requirement to obtain information to perform comparison between
• Centralise database repository to store quantitative and textual information.◦ Data Entry form to gather quantitative
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No. Issue Proposed Functional Requirements
commodity prices to know certain matters such as household income.
data.◦ File Import form to gather quantitative
data in spreadsheet format.◦ HTML Select process to gather data
which are published on website.◦ Dataset Manager to organise the
data / information.• Centralised analysis tool to analyse
quantitative information.◦ Query view to select and display
information from different commodity datasets.
◦ Query Export feature to produce the quantitative data in spreadsheet and document format.
8 Information on agencies' staff position, asset report, disciplinary action are important that are required from KPPK agencies.
• Centralise database repository to store quantitative and textual information.◦ User manager to allow sharing of
information between the divisions and agencies.
◦ Log Statistics to track access and regular updating of information.
◦ Directory form to gather records data.◦ File Import form to gather records data
in spreadsheet format.◦ Dataset Manager to organise the
data / information.◦ Report view to display the records.
9 Information on RMK9 and RMK8 projects and Jawapan Parlimen is required by all divisions.
• Centralise database repository to store quantitative project information and textual Jawapan Parlimen information.◦ User manager to allow sharing of
information between the divisions and agencies.
◦ Log Statistics to track access and regular updating of information.
◦ Dataset Project dimension to organise the data according to RMK Plan and
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No. Issue Proposed Functional Requirements
Project.◦ Data Entry form to gather project data
through manual entry.◦ File Import form to gather project data
in spreadsheet format (eg from SPP2)◦ File Upload form to gather Jawapan
Parlimen document through manual entry.
◦ Report view to display the project data.
◦ Document view to display the list of Jawapan Parlimen documents.
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3. STUDY OF PHASE 1 OF THE KCOMMODITIES SYSTEM
Analysis and study has been done on KCommodities Phase 1 system to identify the
weaknesses and defect of the system. Screenshots of Phase 1 of the KCommodities
system are attached in Appendix A.
No Menu Description Comment1.1 MAIN MENU
– Planted Area
Planted Area User can’t input the data by
batch. One by one only.
User can’t view the data they
have been entered in a list type.
But the users need to remember
and find the data they have been
entered.
After update the data, remain in
the same page without
notification.
Planted areas are not divided by
Region.
1.2 Planted Area by State
No information was displayed if
record doesn’t exist when the
user search. Information shown
when records exist also small and
not attractive.
Users not realize the information
shown because too small.
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No Menu Description Comment1.3 Planted Area by
Sector Total of planted area by sector
shown in unformatted value, ie no
standard of decimal point
assigned and coma between the
numbers. So the table views need
to be formatted in the future.
1.4 Planted Area by District
Data should be formatted to make
sure user of the system can view
easily and arrange the record
accordingly.
1.5 MAIN MENU – Production
Production Input User can’t input the data by
batch. One by one only.
User can’t view the data they
have been entered in a list type.
But the users need to remember
and find the data they have been
entered.
After update the data, remain in
the same page without
notification.
1.6 Production Data Data should be formatted to make
sure user of the system can view
easily.
Add filter to view the data.
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No Menu Description Comment1.7 MAIN MENU
– Price Price Input User should be allowed to input
the data by batch.
User can’t view the data they
have been entered in a list type.
But the users need to remember
and find the data they have been
entered.
After update the data, remain in
the same page without
notification.
1.8 Price Data Data should be formatted to make
sure user of the system can view
easily.
1.9 MAIN MENU – Import
Import Input System should prompt the user if
record they find doesn’t exist.
1.1 Import Data System should displayed the unit
whether the data in tones,
currency or etc.
1.11 MAIN MENU Export
Export Input User should be allowed to input
the data by batch.
User can’t view the data they
have been entered in a list type.
But the users need to remember
and find the data they have been
entered.
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No Menu Description Comment1.12 Export Data Data should be formatted to make
sure user of the system can view
easily.
System should displayed the unit
whether the data in tones,
currency or etc.
1.13 MAIN MENU – Employment
Employment Input User should be allowed to input
the data by batch.
User can’t view the data they
have been entered in a list type.
But the users need to remember
and find the data they have been
entered.
1.14 Employment Data Titles pages should be displayed
clearly and bold the fonts
2.1 STATISTICS & REPORTING
Planted Area Report layout should be more
attractive to view it.
All report using same layout. User
should be given some template to
choose whether in table view, pie
chart, graph chart, line chart or
etc.
Font type should be standardized.
2.2 Planted Area by Sector
2.3 Production by Year & State
2.4 Production by Main Product & Year
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No Menu Description Comment Users should be able to select
other type of graph to display the
record.
Color choose to view the graph
should be more attractive.
2.5 Export Main Product
2.6 Export Value by Country & Year
Report layout should be more
attractive to view it.
All report using same layout. User
should be given some template to
choose whether in table view, pie
chart, graph chart, line chart or
etc.
Font type should be standardized.
2.7 Export Volume by Country & Year
2.8 Import Main Product
2.9 Import Value by Country & Year
2.1 Import Volume by Country & Year
2.11 Local / Farm Gate Price
2.12 F.O.B Price2.13 International Price3.1 BASIC CODE
MANAGEMENT
Product Code Very confusing because Product
and Sub Product are not
displayed in same page. User
need to open different page to
view their sub product.
User can’t update Sub Product
name
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No Menu Description Comment4.1 USER
MANAGEMENT
Create / Manage User
List of registered user can’t be
filter or search by user
2 types of users only ie; Admin
and Biasa. In KComm Phase 2
we will add more types of users.
Information of registered user not
detailed. Common info only.
The system doesn’t have system
prompt after successfully add the
record.
Admin can’t update the user’s
information. Users only can be
deleted from the system.
Supposed admin have the
authority to do such things.4.2 Update Password No password verification to
change the password.
Please refer to Appendix A for screen layout system KCommodities Phase 1.
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4. QUESTIONNAIRE AND WORKSHOP ON THE REQUIREMENTS OF KPPK AND ITS SIX AGENCIES
For KCommodities Phase 2, we will work on these 34 datasets compared to 6
datasets in previous phase. The system will cater the listed data below :
No Data Description1 Planted area Planted areas of each commodities (ha)2 Production Production of every commodity3 Price (local) Local price of commodity 4 Price (world) World price of commodity5 Export commodity Value export of commodities6 Import commodity Value import of commodities7 Employment (local) Number of local employment8 Employment (foreigner) Number of foreigner employment9 Productivity Productivity each commodity
10 Technology Technology used in every commodity11 No of entrepreneur Number of entrepreneur each of
commodity12 R&D Number of R&D produced13 License Number of license14 Competitor (other country) Competitors data from others country15 Competitor (other commodity) Others commodity competitor16 MoU Documents of MoU17 RMK 8 & 9 Documents of RMK 8 & 918 Parliament answer Documents of parliament answer19 Farmer income Income every farmer20 Factory Number of factory21 Policy Documents of standard / policy22 Tariff Value of tariff23 Nontariff barrier Nontariff barrier24 Usage forecast Usage forecast of commodity25 Others country profile Profile others country26 AP Info of AP27 Quota Quota of production28 Current issues Documents of current issues related to
industry 29 Curing barn Info of curing barn
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No Data Description30 Integrated plant Integrated plant available31 Employment issues Employment issues32 Licensing Directory Directory / list of licensing33 Annual report Document of annual report (Document
Repository)34 Location Location of planted area (Location
Manager using Google Map)
4.1. Agency Data
Based on questionnaire result and workshop which has been done previously, every
agency has different type of dataset. We have provided several methods to agencies
to transfer their data to the system. The reason for these data transfer methods is to
avoid duplication of work and to ensure that data is updated on a timely basis.
Wherever possible this consulting team had recommended the use of spreadsheet
(XLS) or published web pages (HTML) as the preferred data transfer method as these
methods provided the most safe and secure methods. The following table documents
the results which has been agreed with the agencies to transfer their data.
4.1.1. Malaysian Palm Oil Board (MPOB)
Bil KEPERLUAN KPPK MPOB 1 Keluasan (ha) Y / T / TB Yes
Data Source DatabaseTime Series YearlyEntity NoProject NoGeography State / DistrictMethod XLS / HTML
2 Pengeluaran Y / T / TB Yes
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Bil KEPERLUAN KPPK MPOBData Source DatabaseTime Series MonthlyEntity NoProject NoGeography State / DistrictMethod XLS / HTML
3 Harga (tempatan) Y / T / TB YesData Source DatabaseTime Series DailyEntity NoProject No
GeographyRegion (SEM MSIA)
Method
XLS / HTML / BUKU PUBLICATION (20052006) / CD(19802004)
4 Harga (dunia) Y / T / TB Yes
Data Source DatabaseTime Series MonthlyEntity NoProject NoGeography NoMethod XLS
5 Eksport Y / T / TB YesData Source DatabaseTime Series MonthlyEntity NoProject NoGeography CountryMethod XLS / HTML
6 Import Y / T / TB YesData Source DatabaseTime Series MonthlyEntity NoProject NoGeography Country
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Bil KEPERLUAN KPPK MPOBMethod XLS
7 Guna tenaga Y / T / TB Yes(tempatan) Data Source Database
Time Series Yearly
EntityYes (Kilang / Ladang)
Project NoGeography State / DistrictMethod XLS
8 Guna tenaga Y / T / TB Yes(asing) Data Source Database
Time Series YearlyEntity NoProject NoGeography State / DistrictMethod XLS
9 Produktiviti Y / T / TB Yes(Hasil/ha/thn) Data Source Database
Time Series MonthlyEntity NoProject NoGeography State / DistrictMethod XLS
10 Tahap Penggunaan Y / T / TB YTeknologi Data Source Database(mekanisasi/otomasi) Time Series
Entity Yes (Kilang / Ladang)
Project Geography Method
11 Bilangan usahawan Y / T / TB Yesterlibat Data Source Database
Time Series Entity Project Geography
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Bil KEPERLUAN KPPK MPOBMethod
12 Bilangan R&D Y / T / TB Yesdihasilkan atau Data Source Databasedikomersilkan Time Series
Entity Project Geography Method
13 Bilangan lesen Y / T / TB Yesdikeluarkan Data Source Database
Time Series MonthlyEntity Project Geography Method
14 Data pesaing Y / T / TB Yes(negara lain)
Data SourceDatabase(Market online)
Time Series YearlyEntity NoProject NoGeography CountryMethod XLS
15 Data pesaing Y / T / TB Yes(komoditi/produk lain)
Data SourceDatabase (Market & Tariff online)
Time Series YearlyEntity NoProject NoGeography CountryMethod XLS
16 MoU Y / T / TB YData Source DatabaseTime Series Entity Project Geography
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Bil KEPERLUAN KPPK MPOBMethod
17 Projek RMK 8 & 9 Y / T / TB Tidak pastiData Source Time Series Entity Project Geography Method
18 Jawapan Parlimen Y / T / TB YesData Source Manually keyinTime Series Entity Project Geography Method
19 Pendapatan Pekebun Y / T / TB YesKecil Data Source
Time Series Survey BEPI (TE) sekali sahaja
Entity Project Geography Method
20 Bilangan Kilang Y / T / TB YesData Source DatabaseTime Series MonthlyEntity Yes (Ikut kilang)Project NoGeography NoMethod XLS
21 Dasar / Polisi Y / T / TB YData Source mTime Series Entity Project Geography
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Bil KEPERLUAN KPPK MPOBMethod
22 Tariff Y / T / TB Tidak pastiData Source DatabaseTime Series YearlyEntity NoProject NoGeography CountryMethod XLS
23 Non tariff barier Y / T / TB Tidak pastiData Source Manually keyinTime Series (when get info)Entity NoProject NoGeography CountryMethod UP (Ms Word)
24 Unjuran penggunaan Y / T / TB T(consumption prediction) Data Source Database Time Series 3 years Entity Project Geography Method
25 Maklumat negara Y / T / TBlain (profil) Data Source
Time SeriesEntityProjectGeographyMethod
26 Maklumat AP Y / T / TBData SourceTime Series Entity Project Geography Method
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Bil KEPERLUAN KPPK MPOB27 Kuota pengeluaran Y / T / TB No
Data SourceTime Series Entity Project Geography Method
28 Isuisu semasa Y / T / TB Yesberkaitan industri Data Source Manually keyin
Time Series Entity Project Geography Method
29 Curing barn Y / T / TB Tidak pastichange to Data Source facilities provided(perkhidmatan yg disediakan)
Time Series
Entity Project Geography Method
30 Tanaman integrasi/ternakan integrasi Y / T / TB Yes* Change to Data Source Database BEPIMaklumat Integrasi Time Series Survey sekali shj
2006 Entity Project Geography Method
31 Maklumat perjawatan Y / T / TB Noagensi Data Source
Time Series Entity Project Geography
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Bil KEPERLUAN KPPK MPOBMethod
32 Direktori / senarai Y / T / TB Yespelesenan Data Source Database
Time SeriesEntity Project NoGeography NoMethod XLS
33 Laporanlaporan Y / T / TB Ykewangan / tahunan Data Source Databaseagensi Time Series
Entity Project Geography Method
34 Lokasi kawasan Y / T / TB Ypengeluaran dalam Data Source Databasenegara Time Series Yearly* REPLACED WITH Entity Cost of Production Project Geography Method
KPPK’s responsibility to upload the data
4.1.2. Malaysian Rubber Board (MRB)
Bil KEPERLUAN KPPK MRB 1 Keluasan (ha) Y / T / TB Yes
Data Source DatabaseTime Series YearlyEntity YesProject No
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Bil KEPERLUAN KPPK MRBGeography RegionMethod XLS / Manually
2 Pengeluaran Y / T / TB YesData Source DatabaseTime Series MonthlyEntity YesProject NoGeography RegionMethod XLS / Manually
3 Harga (tempatan) Y / T / TB YesData Source DatabaseTime Series DailyEntity NoProject NoGeography NoMethod XLS / HTML
4 Harga (dunia) Y / T / TB Not RelatedData SourceTime SeriesEntityProjectGeographyMethod
5 Eksport Y / T / TB YesData Source DatabaseTime Series MonthlyEntity NoProject No
GeographyCountry / Continent
Method XLS
6 Import Y / T / TB YesData Source DatabaseTime Series MonthlyEntity NoProject NoGeography Country /
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Bil KEPERLUAN KPPK MRBContinent
Method XLS
7 Guna tenaga Y / T / TB Yes(tempatan) Data Source Database
Time Series YearlyEntity NoProject NoGeography CountryMethod XLS / Manually
8 Guna tenaga Y / T / TB Yes(asing) Data Source Manually keyin
Time Series YearlyEntity NoProject NoGeography CountryMethod XLS / Manually
9 Produktiviti Y / T / TB Yes(Hasil/ha/thn) Data Source Manually keyin
Time Series YearlyEntity YesProject NoGeography CountryMethod XLS / Manually
10 Tahap Penggunaan Y / T / TB YTeknologi Data Source Manually keyin(mekanisasi/otomasi) Time Series Yearly
Entity YesProject YesGeography Region / State /
District / Parliament / State Assembly
Method XLS
11 Bilangan usahawan Y / T / TB Yesterlibat Data Source Database
Time Series YearlyEntity Yes
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Bil KEPERLUAN KPPK MRBProject No
GeographyCountry / Region / State
Method XLS / HTML
12 Bilangan R&D Y / T / TB Yesdihasilkan atau Data Source Manually keyindikomersilkan Time Series Yearly
Entity YesProject YesGeography CountryMethod XLS
13 Bilangan lesen Y / T / TB Yesdikeluarkan Data Source Database
Time Series QuarterlyEntity YesProject NoGeography Region Method HTML
14 Data pesaing Y / T / TB(negara lain) Data Source
Time SeriesEntityProjectGeographyMethod
15 Data pesaing Y / T / TB(komoditi/produk lain) Data Source
Time SeriesEntityProjectGeographyMethod
16 MoU Y / T / TBData SourceTime SeriesEntityProject
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Bil KEPERLUAN KPPK MRBGeographyMethod
17 Projek RMK 8 & 9 Y / T / TBData Source Time Series Entity Project Geography Method
18 Jawapan Parlimen Y / T / TBData SourceTime Series Entity Project Geography Method
19 Pendapatan Pekebun Y / T / TB YesKecil Data Source Manually
Time Series YesEntity YesProject YesGeography Region / State /
District / Parliament / State Assembly
Method XLS
20 Bilangan Kilang Y / T / TB YesData Source DatabaseTime Series YearlyEntity YesProject NoGeography Region / StateMethod XLS
21 Dasar / Polisi Y / T / TBData SourceTime SeriesEntity
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Bil KEPERLUAN KPPK MRBProjectGeographyMethod
22 Tariff Y / T / TBData SourceTime SeriesEntityProjectGeographyMethod
23 Non tariff barier Y / T / TBData SourceTime SeriesEntityProjectGeographyMethod
24 Unjuran penggunaan Y / T / TB Yes(consumption prediction) Data Source Manually Time Series Monthly Entity Yes Project No Geography Country Method XLS
25 Maklumat negara Y / T / TBlain (profil) Data Source
Time Series Entity Project Geography Method
26 Maklumat AP Y / T / TBData Source Time Series Entity Project Geography
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Bil KEPERLUAN KPPK MRBMethod
27 Kuota pengeluaran Y / T / TB Not RelatedData SourceTime Series Entity Project Geography Method
28 Isuisu semasa Y / T / TB Yesberkaitan industri Data Source Manually keyin
Time Series MonthlyEntity NoProject NoGeography NoMethod Upload
29 Curing barn Y / T / TB Yeschange to Data Source Manually keyin facilities provided (perkhidmatan yg disediakan)
Time Series Yearly
Entity No Project No Geography CSV Method Upload
30 Tanaman integrasi/ternakan integrasi Y / T / TB Yes* Change to Data Source Manually keyinMaklumat Integrasi Time Series Yearly Entity No Project No Geography Country Method Upload
31 Maklumat perjawatan Y / T / TB Yesagensi Data Source Manually keyin
Time Series QuarterlyEntity YesProject No
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Bil KEPERLUAN KPPK MRBGeography No Method Manually
32 Direktori / senarai Y / T / TB Yespelesenan Data Source Database
Time Series QuarterlyEntity YesProject NoGeography RegionMethod HTML
33 Laporanlaporan Y / T / TB Yeskewangan / tahunan Data Source Manually keyinagensi Time Series Quarterly / Yearly
Entity NoProject NoGeography No Method Upload
34 Lokasi kawasan Y / T / TB Ypengeluaran dalam Data Source Manually keyinnegara Time Series Yearly* REPLACED WITH Entity YesCost of Production Project Yes Geography Region / State Method XLS
KPPK’s responsibility to upload the data
4.1.3. Malaysian Timber Industries Board (MTIB)
Bil KEPERLUAN KPPK MTIB 1 Keluasan (ha) Y / T / TB Yes
Data SourceExternal (Jab Hutan)
Time Series Yearly
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Bil KEPERLUAN KPPK MTIBEntity YesProject NoGeography Region / StateMethod CSV / XLS /
Upload
2 Pengeluaran Y / T / TB YesData Source External (Jab
Hutan)Time Series YearlyEntity NoProject NoGeography Region / StateMethod CSV / XLS /
Upload
3 Harga (tempatan) Y / T / TB YesData Source DatabaseTime Series MonthlyEntity YesProject NoGeography Country / RegionMethod CSV / XLS /
System Data
4 Harga (dunia) Y / T / TB YesData Source DatabaseTime Series MonthlyEntity YesProject NoGeography Country / RegionMethod CSV / XLS /
System Data
5 Eksport Y / T / TB YesData Source External /
Database / Manually
Time Series Month / YearEntity YesProject NoGeography Contry / Region /
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Bil KEPERLUAN KPPK MTIBState
Method XLS / System Data
6 Import Y / T / TB Yes
Data Source
External / Database / Manually
Time Series Monthly / YearlyEntity YesProject No
GeographyContry / Region / State
Method XLS / System Data
7 Guna tenaga Y / T / TB Yes(tempatan)
Data Source
External / Database / Manually
Time Series YearlyEntity YesProject NoGeography Country / StateMethod XLS / System Data
8 Guna tenaga Y / T / TB Yes(asing) Data Source External /
Database / Manually
Time Series YearlyEntity YesProject NoGeography Country / StateMethod XLS
9 Produktiviti Y / T / TB Not Related(Hasil/ha/thn) Data Source
Time Series Entity Project Geography Method
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Bil KEPERLUAN KPPK MTIB10 Tahap Penggunaan Y / T / TB Yes
Teknologi Data SourceDatabase / Manually
(mekanisasi/otomasi) Time Series YearlyEntity YesProject NoGeography CountryMethod XLS
11 Bilangan usahawan Y / T / TB Yesterlibat
Data SourceDatabase / Manually
Time Series Monthly / YearlyEntity YesProject No
GeographyCountry / Region / State
Method XLS / System Data
12 Bilangan R&D Y / T / TB Yesdihasilkan atau Data Source Manuallydikomersilkan Time Series Yearly
Entity NoProject YesGeography Country / RegionMethod Manually
13 Bilangan lesen Y / T / TB Yesdikeluarkan Data Source Database
Time Series DailyEntity YesProject No
GeographyCountry / Region / State
Method System Data
14 Data pesaing Y / T / TB Yes(negara lain) Data Source External / Manually
/ DatabaseTime Series YearlyEntity YesProject No
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Bil KEPERLUAN KPPK MTIBGeography Continent /
CountryMethod Upload / System
Data / XLS
15 Data pesaing Y / T / TB Yes(komoditi/produk lain) Data Source External / Manually
Time Series Monthly / YearlyEntity YesProject NoGeography Country / RegionMethod Upload
16 MoU Y / T / TB YesData Source ManuallyTime Series YearlyEntity YesProject YesGeography CountryMethod Upload / Manually
17 Projek RMK 8 & 9 Y / T / TB YesData Source ExternalTime Series Monthly / YearlyEntity YesProject No
GeographyCountry / Region / State / Parliament
Method Upload / Manually
18 Jawapan Parlimen Y / T / TB YesData Source ManuallyTime Series QuarterlyEntity YesProject NoGeographyMethod Manually
19 Pendapatan Pekebun Y / T / TB Not relatedKecil Data Source
Time Series Entity
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Bil KEPERLUAN KPPK MTIBProject Geography Method
20 Bilangan Kilang Y / T / TB Yes
Data SourceDatabase / Manually
Time Series YearlyEntity YesProject NoGeography Country / RegionMethod System Data / XLS
21 Dasar / Polisi Y / T / TB YesData Source ManuallyTime Series YearlyEntity YesProject NoGeography Country / RegionMethod Upload
22 Tariff Y / T / TB YesData Source External / ManuallyTime Series YearlyEntity YesProject NoGeography Continent /
CountryMethod Upload / Manually
23 Non tariff barier Y / T / TB YesData Source ExternalTime Series YearlyEntity YesProject NoGeography CountryMethod X LS / Upload
24 Unjuran penggunaan Y / T / TB Yes(consumption prediction) Data Source External Time Series Yearly Entity Yes
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Bil KEPERLUAN KPPK MTIB Project No Geography Country / Region Method XLS / Upload
25 Maklumat negara Y / T / TB Yeslain (profil) Data Source External
Time Series YearlyEntity YesProject NoGeography Country / RegionMethod Upload
26 Maklumat AP Y / T / TB YesData Source DatabaseTime Series DailyEntity YesProject NoGeography StateMethod System Data
27 Kuota pengeluaran Y / T / TB Not relatedData Source Time Series Entity Project Geography Method
28 Isuisu semasa Y / T / TB Yesberkaitan industri
Data SourceExternal / Database
Time Series Monthly / YearlyEntity YesProject No
GeographyCountry / Region / State
MethodUpload / System Data
29 Curing barn Y / T / TB Not related
change to Data Source facilities provided Time Series
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Bil KEPERLUAN KPPK MTIB(perkhidmatan yg disediakan) Entity Project Geography Method
30 Tanaman integrasi/ternakan integrasi
Y / T / TB Not related
* Change to Data Source Maklumat Integrasi Time Series Entity Project Geography Method
31 Maklumat perjawatan Y / T / TB Yesagensi Data Source Database
Time SeriesQuarterly / Half year / Yearly
Entity YesProject YesGeography Region / StateMethod System Data
32 Direktori / senarai Y / T / TB Yespelesenan Data Source Database
Time Series DailyEntity YesProject No
GeographyCountry / Region / State
Method System Data
33 Laporanlaporan Y / T / TB Yeskewangan / tahunan Data Source Manuallyagensi Time Series Monthly / Yearly
Entity YesProject NoGeography Country / StateMethod System Data /
Upload / CLS /
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Bil KEPERLUAN KPPK MTIBXLS
34 Lokasi kawasan Y / T / TB Not Related
pengeluaran dalam Data Sourcenegara Time Series* REPLACED WITH EntityCost of Production Project Geography Method
KPPK’s responsibility to upload the data
4.1.4. National Tobacco Board (NTB)
Bil KEPERLUAN KPPK LTN 1 Keluasan (ha) Y / T / TB Yes
Data Source ManuallyTime Series YearlyEntity NoProject NoGeography DistrictMethod CLS / XLS
2 Pengeluaran Y / T / TB YesData Source ManuallyTime Series MonthlyEntity NoProject NoGeography DistrictMethod CSV / XLS
3 Harga (tempatan) Y / T / TB YesData Source ManuallyTime Series YearlyEntity
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Bil KEPERLUAN KPPK LTNProject NoGeography NoMethod Manually
4 Harga (dunia) Y / T / TB NoData Source Time Series Entity Project Geography Method
5 Eksport Y / T / TB Not RelatedData Source Time Series Entity Project Geography Method
6 Import Y / T / TB YesData Source ManuallyTime Series Entity Project Geography Method
7 Guna tenaga Y / T / TB Yes(tempatan) Data Source Manually
Time Series YearlyEntity Project NoGeography DistrictMethod CSV / XLS
8 Guna tenaga Y / T / TB Not Related(asing) Data Source
Time Series Entity Project Geography
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Bil KEPERLUAN KPPK LTNMethod
9 Produktiviti Y / T / TB Yes(Hasil/ha/thn) Data Source Manually
Time Series MonthlyEntity Project NoGeography DistrictMethod CSV / XLS
10 Tahap Penggunaan Y / T / TB YesTeknologi Data Source Monthly(mekanisasi/otomasi) Time Series Half Year
Entity NoProject NoGeography StateMethod Upload
11 Bilangan usahawan Y / T / TB Yesterlibat Data Source Maually
Time Series YearlyEntity Project NoGeography DistrictMethod CSV / XLS
12 Bilangan R&D Y / T / TB Not Relateddihasilkan atau Data Source dikomersilkan Time Series
Entity Project Geography Method
13 Bilangan lesen Y / T / TB Yesdikeluarkan Data Source Database
Time Series YearlyEntity Project GeographyMethod System Data
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Bil KEPERLUAN KPPK LTN14 Data pesaing Y / T / TB Not Related
(negara lain)Data Source Time Series Entity Project Geography Method
15 Data pesaing Y / T / TB No(komoditi/produk lain) Data Source
Time Series Entity Project Geography Method
16 MoU Y / T / TB YesData Source ManuallyTime Series Entity NoProject NoGeography NoMethod Upload
17 Projek RMK 8 & 9 Y / T / TB YesData Source ManuallyTime Series Entity Project Geography Method
18 Jawapan Parlimen Y / T / TB YesData Source ManuallyTime Series Entity Project Geography Method
19 Pendapatan Pekebun Y / T / TB Yes
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Bil KEPERLUAN KPPK LTNKecil Data Source Manually
Time Series MonthlyEntity Project NoGeography DistrictMethod CSV / XLS
20 Bilangan Kilang Y / T / TB YesData Source ManuallyTime Series YearlyEntity Project NoGeography StateMethod Upload
21 Dasar / Polisi Y / T / TB YesData Source ManuallyTime Series Entity Project Geography Method
22 Tariff Y / T / TB YesData Source ManuallyTime Series Entity Project Geography Method
23 Non tariff barier Y / T / TB YesData Source ManuallyTime Series Entity Project Geography Method
24 Unjuran penggunaan Y / T / TB Yes(consumption prediction) Data Source Manually Time Series
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Bil KEPERLUAN KPPK LTN Entity Project Geography Method
25 Maklumat negara Y / T / TB Yeslain (profil) Data Source Manually
Time Series Entity Project Geography Method
26 Maklumat AP Y / T / TB YesData Source ManuallyTime Series Entity Project Geography Method
27 Kuota pengeluaran Y / T / TB YesData Source ManuallyTime Series Entity Project Geography Method
28 Isuisu semasa Y / T / TB Yesberkaitan industri Data Source Manually
Time Series Entity Project Geography Method
29 Curing barn Y / T / TB Yeschange to Data Source Manuallyfacilities provided(perkhidmatan yg disediakan)
Time Series Yearly
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Bil KEPERLUAN KPPK LTN Entity Project No Geography District Method CSV / XLS
30 Tanaman integrasi/ternakan integrasi
Y / T / TB Yes
* Change to Data Source ManuallyMaklumat Integrasi Time Series Weekly Entity Project No Geography District Method CSV / XLS
31 Maklumat perjawatan Y / T / TB Yesagensi Data Source Manually
Time Series MonthlyEntity YesProject YesGeography NoMethod CSV / XLS
32 Direktori / senarai Y / T / TB Yespelesenan Data Source Manually
Time Series Entity Project Geography Method
33 Laporanlaporan Y / T / TB Yeskewangan / tahunan Data Source Manuallyagensi Time Series Yearly
Entity NoProject NoGeography NoMethod Upload
34 Lokasi kawasan Y / T / TB Yespengeluaran dalam Data Source Manuallynegara Time Series Monthly* REPLACED WITH Entity
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Bil KEPERLUAN KPPK LTNCost of Production Project No Geography District Method CSV / XLS
KPPK’s responsibility to upload the data
4.1.5. Malaysian Cocoa Board (MCB)
Bil KEPERLUAN KPPK MCB 1 Keluasan (ha) Y / T / TB Yes
Data Source DatabaseTime Series YearlyEntity Project Geography RegionMethod CSV / XLS
2 Pengeluaran Y / T / TB YesData Source DatabaseTime Series Quarterly / Half
year / YearlyEntity Project Geography RegionMethod CSV / XLS
3 Harga (tempatan) Y / T / TB YesData Source DatabaseTime Series DailyEntityProject Geography DistrictMethod CSV / XLS
4 Harga (dunia) Y / T / TB YesData Source Database
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Bil KEPERLUAN KPPK MCBTime Series Quarterly / YearlyEntity Project Geography CountryMethod CSV / XLS
5 Eksport Y / T / TB YesData Source DatabaseTime Series Monthly / YearlyEntity Project Geography CountryMethod CSV / XLS
6 Import Y / T / TB YesData Source DatabaseTime Series Monthly / YearlyEntity Project Geography CountryMethod CSV / XLS
7 Guna tenaga Y / T / TB Yes(tempatan) Data Source Manually
Time Series YearlyEntity Project Geography Method Manually
8 Guna tenaga Y / T / TB Yes(asing) Data Source Manually
Time Series MonthlyEntity YesProject Geography Method Manually
9 Produktiviti Y / T / TB Yes(Hasil/ha/thn) Data Source Manually
Time Series YearlyEntity
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Bil KEPERLUAN KPPK MCBProject Geography Method Manually
10 Tahap Penggunaan Y / T / TB YesTeknologi Data Source Manually(mekanisasi/otomasi) Time Series Yearly
Entity Project Geography Method Manually
11 Bilangan usahawan Y / T / TB Yesterlibat Data Source Manually
Time Series Half year / YearlyEntity Project Geography Method Manually
12 Bilangan R&D Y / T / TB Yesdihasilkan atau Data Source Manuallydikomersilkan Time Series
Entity Project Geography Method Manually
13 Bilangan lesen Y / T / TB Yesdikeluarkan Data Source Database
Time SeriesQuarterly / Half year / Yearly
Entity Project Geography StateMethod CSV / XLS
14 Data pesaing Y / T / TB Yes(negara lain)
Data SourceManually
Time Series Entity
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Bil KEPERLUAN KPPK MCBProject Geography Method
15 Data pesaing Y / T / TB No(komoditi/produk lain)
Data Source Time Series Entity Project Geography Method
16 MoU Y / T / TB YesData Source ManuallyTime Series Entity Project Geography Method Upload
17 Projek RMK 8 & 9 Y / T / TB YesData Source ManuallyTime Series YearlyEntity Project Geography Method CSV / XLS
18 Jawapan Parlimen Y / T / TB YesData Source ManuallyTime Series Entity Project Geography Method Upload
19 Pendapatan Pekebun Y / T / TB YesKecil Data Source Manually
Time Series YearlyEntity Project
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Bil KEPERLUAN KPPK MCBGeography RegionMethod Manually
20 Bilangan Kilang Y / T / TB YesData Source ManuallyTime Series Entity Project Geography Method Manually
21 Dasar / Polisi Y / T / TB YesData Source ManuallyTime Series Entity Project Geography Method Upload
22 Tariff Y / T / TB YesData Source ManuallyTime Series Entity Project Geography Method CSV / XLS
23 Non tariff barier Y / T / TB YesData Source ManuallyTime Series Entity Project Geography Method
24 Unjuran penggunaan Y / T / TB Yes(consumption prediction) Data Source Manually Time Series Entity Project Geography Method
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Bil KEPERLUAN KPPK MCB
25 Maklumat negara Y / T / TB Yeslain (profil) Data Source Manually
Time Series Entity Project Geography Method Upload
26 Maklumat AP Y / T / TB NoData Source ManuallyTime Series Entity Project Geography Method
27 Kuota pengeluaran Y / T / TB Not RelatedData Source Time Series Entity Project Geography Method
28 Isuisu semasa Y / T / TB Yesberkaitan industri Data Source Manually
Time Series Entity Project Geography Method Upload
29 Curing barn Y / T / TB Not relatedchange to Data Source facilities provided(perkhidmatan yg disediakan)
Time Series
Entity Project Geography Method
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Bil KEPERLUAN KPPK MCB
30 Tanaman integrasi/ternakan integrasi Y / T / TB Yes* Change to Data Source ManuallyMaklumat Integrasi Time Series Entity Project Geography Method Upload
31 Maklumat perjawatan Y / T / TB Yesagensi Data Source Manually
Time Series Entity Project Geography Method Upload
32 Direktori / senarai Y / T / TB Yespelesenan Data Source Database
Time Series Entity Project Geography Method CSV / XLS
33 Laporanlaporan Y / T / TB Yeskewangan / tahunan Data Source Manuallyagensi Time Series
Entity Project Geography Method Upload
34 Lokasi kawasan Y / T / TB Yespengeluaran dalam Data Source Manuallynegara Time Series * REPLACED WITH Entity Cost of Production Project Geography Region Method CSV / XLS
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KPPK’s responsibility to upload the data
4.1.6. Malaysian Pepper Board (MPB)
Bil KEPERLUAN KPPK MPB 1 Keluasan (ha) Y / T / TB Yes
Data Source DatabaseTime Series YearlyEntity NoProject NoGeography StateMethod System Data
2 Pengeluaran Y / T / TB YesData Source DatabaseTime Series YearlyEntity Project Geography StateMethod System Data
3 Harga (tempatan) Y / T / TB YesData Source DatabaseTime Series DailyEntity YesProject NoGeography StateMethod System Data
4 Harga (dunia) Y / T / TB YesData Source DatabaseTime Series MonthlyEntity Project Geography CountryMethod System Data
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Bil KEPERLUAN KPPK MPB5 Eksport Y / T / TB Yes
Data Source DatabaseTime Series MonthlyEntity NoProject NoGeography CountryMethod System Data
6 Import Y / T / TB YesData Source DatabaseTime Series MonthlyEntity NoProject NoGeography CountryMethod System Data
7 Guna tenaga Y / T / TB Yes(tempatan) Data Source Database
Time Series YearlyEntity Project Geography StateMethod System Data
8 Guna tenaga Y / T / TB Not related(asing) Data Source
Time SeriesEntityProjectGeographyMethod
9 Produktiviti Y / T / TB Y(Hasil/ha/thn) Data Source Database
Time Series YearlyEntity Project Geography StateMethod System Data
10 Tahap Penggunaan Y / T / TB YTeknologi Data Source Database
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Bil KEPERLUAN KPPK MPB(mekanisasi/otomasi) Time Series Yearly
EntityYes (Kilang / Ladang)
Project No Geography StateMethod System Data
11 Bilangan usahawan Y / T / TB Yesterlibat Data Source Database
Time Series YearlyEntity Project Geography DistrictMethod CSV
12 Bilangan R&D Y / T / TBdihasilkan atau Data Sourcedikomersilkan Time Series
Entity Project Geography Method
13 Bilangan lesen Y / T / TB Yesdikeluarkan Data Source Database
Time Series YearlyEntity Project Geography DistrictMethod System Data
14 Data pesaing Y / T / TB Yes(negara lain)
Data SourceDatabase
Time Series YearlyEntity Project Geography CountryMethod System Data
15 Data pesaing Y / T / TB Yes(komoditi/produk lain) Data Source
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Bil KEPERLUAN KPPK MPBTime Series YearlyEntity Project Geography CountryMethod System Data
16 MoU Y / T / TB YesData Source ManuallyTime Series Entity Project Geography Method Upload
17 Projek RMK 8 & 9 Y / T / TB YesData Source KPPKTime Series Entity Project Geography Method
18 Jawapan Parlimen Y / T / TB YesData Source KPPKTime Series Entity Project Geography Method
19 Pendapatan Pekebun Y / T / TB YesKecil Data Source Manually
Time Series
Entity Project Geography StateMethod Manually
20 Bilangan Kilang Y / T / TB YesData Source Manually keyin
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Bil KEPERLUAN KPPK MPBTime Series YearlyEntity Project Geography Method Manually
21 Dasar / Polisi Y / T / TBData SourceTime SeriesEntityProjectGeographyMethod
22 Tariff Y / T / TB YesData Source DatabaseTime Series Entity Project Geography CountryMethod System Data
23 Non tariff barier Y / T / TB YesData Source ManuallyTime Series Entity Project Geography CountryMethod System Data
24 Unjuran penggunaan Y / T / TB Yes(consumption prediction) Data Source Database Time Series Yearly Entity Project Geography Country Method System Data
25 Maklumat negara Y / T / TB Yeslain (profil) Data Source Manually
Time Series YesEntity
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Bil KEPERLUAN KPPK MPBProject Geography CountryMethod System Data
26 Maklumat AP Y / T / TB Not relatedData SourceTime Series Entity Project Geography Method
27 Kuota pengeluaran Y / T / TB Not relatedData Source Time Series Entity Project Geography Method
28 Isuisu semasa Y / T / TB Yesberkaitan industri Data Source Database
Time Series Entity Project Geography CountryMethod System data
29 Curing barn Y / T / TB Yeschange to Data Source Manuallyfacilities provided(perkhidmatan yg disediakan)
Time Series
Entity Project Geography Method Manually
30 Tanaman integrasi/ternakan integrasi
Y / T / TB Not related
* Change to Data Source Maklumat Integrasi Time Series
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Bil KEPERLUAN KPPK MPB Entity Project Geography Method
31 Maklumat perjawatan Y / T / TBagensi Data Source
Time Series Entity Project Geography Method
32 Direktori / senarai Y / T / TB Yespelesenan Data Source Database
Time Series Entity Project Geography Method System Data
33 Laporanlaporan Y / T / TB Yeskewangan / tahunan Data Source Databaseagensi Time Series Monthly
Entity Project Geography Method CSV / XLS
34 Lokasi kawasan Y / T / TB Yespengeluaran dalam Data Source Databasenegara Time Series Yearly* REPLACED WITH Entity Cost of Production Project Geography State Method System Data
KPPK’s responsibility to upload the data
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5. PROPOSED PHASE 2 IMPLEMENTATION STRATEGY INCLUDING CURRENT AND
FUTURE REQUIREMENTS
This part of the study uses a topdown method to understand the organizational
objectives, identifying the key parameters, the information needs, the information
lifecycle and the required tools necessary for a business intelligence view of the
commodities market. Based on the outline of this study we have identified what K
Commodities should have within this phase and what it should plan for in the near
future. The main factor distinguishing this is the impact of externalities on the
complexity and timeline which will require a larger commitment and the establishment
of a dedicated unit. These define the following overall implementation strategy
roadmap.
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5.1. Organisational Objectives
KPPK has a well defined organisational vision, mission and objectives statement. It
has a clear vision statement on where it wants to be as a centre of excellence, a
corresponding mission statement on achieving it via competitiveness; and objectives
which employ focused strategies.
The above objectives fulfills the basic SMART criteria of project management that is
Specific, Measurable, Attainable, Relevant and Timebound. The SMART criteria
allows a structured method for planning on what needs to be done, as well as,
assessing the achievement based on targets. These targets will form the datasets that
the KCommodities system need to gather information upon.
Based on the objectives defined by KPPK, there are four broad type of datasets:
1. Value added and contribution to the national Gross Domestic Production (GDP)
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2. Activities and global ranking of activities related to research and development
(R&D), technology and services
3. Marketing activities and market share in key markets
4. Industrial efficiency, productivity, quality and sustainability
Within each of the dataset types, there are further tiers of datasets which can either be:
1. absolute value (such as the value of exports for the month) which sums up to a
total, or
2. indirect indicators (such as the number of exhibitors in a trade show) which
provides a correlated index of the measure of achievement.
The ability to develop multitier datasets is a builtin feature of the eIDA framework
which will form the core foundation of KCommodities.
5.2. Dimensions to the Dataset
Within these datasets there are certain characteristics that are shared across the
datasets which are referred to as dimensions. An example of a dimension is time which
provides a common level of comparison between datasets such as the value of exports
for the month of March 2009 and the number of exhibitors in a trade show held in
March 2009. The following illustrates a typical dimensional model:
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A dimensional dataset is required to support the analysis of business trends and
projections. This type of informational processing is known as online analytical
processing (OLAP) or decisionsupport processing. In contrast, operational business
applications are based on online transaction processing (OLTP) and is tuned for
operational needs which can retrieve small number of selected records quickly, but can
be slow when retrieving large number of records and summarising the data.
In identifying the dimensions, a macro view was adopted which looked at the total
economic valuechain. This view was adopted as it is the most appropriate for a
Ministry whose role is in formulating national strategies and policies. Particularly for
agriculture based commodities which have wide ranging socioeconomic impact on
firm and household behavior.
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The above model illustrates how fiscal expenditure are implemented through
government, agencies, and projects. These fiscal expenditures will then impact the
domestic economy via the agriculture, industry, trade and export sectors. These
economic activities translate into employment and the consumption of goods and
services in the society who will, in a democratic country, influence the national agenda.
Within this economic valuechain there are several dimensions:
1. Datasets to represent the sectoral categorisation of activities
2. Time to measure the efficiency of the flow in the valuechain
3. Entities to represent public and private sector organisations
4. Items to represent the goods and services produced by the respective sectors
5. Projects with finite start and end dates and the impact on the value chain
6. Geographies to denote boundaries, markets and competitors
7. Measure of the expenditure, production or consumption
8. Value whether in monetary or physical terms
The ability to develop multitier OLAP dimensions and summation of data by
dimension is a builtin feature of the eIDA framework which will form the core
foundation of KCommodities.
5.3. Project Management LifeCycle
As outlined in the above model, projects represents an important component of fiscal
measures that are used by the government to stimulate and direct the economy
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towards certain objectives. A project is defined as a set of activities with a finite start
and end dates. A project would typically consist of five distinct phases:
Within each of the five phases, there are several informational needs that require
certain activities to be done by the project manager.
5.3.1. Project Evaluation
The project evaluation phase deals primarily on deciding whether to implement a
certain project. To assist the decision maker, the project manager need to have a
model (to understand how something works – such as the interaction of buyers and
sellers) and a forecast (to predict the future outcome – such as the volume of exports
in twelve months time).
Modelling requires a multidisciplinary team of strategist, analyst and subject matter
experts. The common techniques that are used during modelling include:
1. Strategy
1. Organisational alignment that the project is consistent with the overall
organisational objectives
2. Current environment analysis using Strength, Weakness, Opportunities,
Threats (SWOT) matrix or Political, Economic, Social, Technological,
Environmental, and Legal (PESTEL) factors.
3. Strategies formulation by using Porter's Five Forces Analysis : Competitors,
Suppliers, Buyers, New Entrant, and Product Substitute.
2. Subject Matter
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1. Technical feasibility to determine whether it can be done
2. Operational model to identify what are the resources required
3. Business model to identify how is the output to be delivered to market
Modelling requires an expert systems feature which tries to capture and mimic the
knowledge of a subject matter expert. How good is an expert system depends on the
problem solving template suitable to the nature of the project, the relevancy of the
knowledgebase and the rules based algorithm. Commercially available business
modelling template are usually in the form of spreadsheets.
Forecasting attempts to predict what will happen in the future and is typically presented
in number value, such as the net cashflow, return on investment (ROI), internal rate of
return (IRR), net present value (NPV) or probability of outcome (odds ratio). However a
lot of factors (such as the PESTEL factors) and variables need to go into forecasting.
Some of the factors are discrete (such as capitalist, socialist, closed or open economic
systems) while some others are interval (such as population size).
Modern forecasting systems would typically use statistical methods to compute the
probability of outcome based on the pattern of historical data and dependency of
factors. Some of the commonly used business statistical methods used are moving
average, linear trend model, exponential trend model, and autoregression. These
methods are available using Microsoft Excel spreadsheet Data Analysis addin.
A review of the statistical modelling templates available from published journals were
done. These were the results most closely relevant to the sectors that KPPK operate
in:
a) Black Walnut financial model was prepared by Larry Godsey, Center
Economist, University of Missouri Center for Agroforestry to provide a simplified
decision model for land owners to assess the economic returns of planting
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trees alongside agriculture farmland. It is designed to assist potential growers
make decision about tree spacing, nut harvest, and whether to use improved
(grafted) or unimproved trees. The model does not claim to accurately show
tree growth characteristics at future points in time. However, it does use a
simple algorithm to make estimates about future nut production and tree
diameters. The model should be used to consider how certain management
decisions, i.e. tree spacing, will affect the financial performance of the
plantation in terms of potential increases and decreases in net present value,
internal rate of return and annual equivalent value. All financial numbers are
given on a per acre basis. The model is available in spredsheet format.
Reference : http://www.finance30.com/forum/topic/show?id=1987892%3ATopic
%3A15990
b) USDA Season Average Price Forecast model provides three Excel file
spreadsheet models that use futures prices to forecast the U.S. season
average price received and the implied CCP for three major field crops (corn,
soybeans, and wheat). Users can view the model forecasts or create their own
forecast by inserting different values for futures prices, basis values, or
marketing weights. Example computations and data are provided. The model is
available in spreadsheet format. Reference :
http://www.ers.usda.gov/Data/PriceForecast/
c) NeuroXL Classifier is a fast, powerful and easytouse neural network software
tool for classifying data in Microsoft Excel. Designed to aid experts in realworld
data mining and pattern recognition tasks, it hides the underlying complexity of
neural network processes while providing graphs and statistics for the user to
easily understand results. NeuroXL Classifier uses only proven algorithms and
techniques, and integrates seamlessly with Microsoft Excel. This statistical
analysis tool is available in spreadsheet format. Reference :
http://www.neuroxl.com/white_paper_neural_networks.htm
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d) StatTools gives the industrystandard data analysis tool— Microsoft Excel—a
new, powerful statistics toolset! StatTools is a Microsoft Excel statistics addin,
so you analyze data in Excel and work in the familiar Microsoft Office
environment. StatTools brings you the best of two worlds: Microsoft Office
easeofuse, and robust statistical power. Plus, StatTools is developed by
Palisade Corporation, the world leader in analytical solutions that addin to
Microsoft Excel. This statistical analysis tool is available in spreadsheet format.
Reference :http://www.palisade.com/stattools/
The MOLAP module allows data to be extracted using MS Excel (or OO.o Calc).
Combined with add on MS Excel templates, several forecasting models can be used to
analyse the data. Using statistical forecasting techniques and vertical industry
modelling templates, users can generate their own expert systems. There is also an
advantage in that many users are already familiar and comfortable with using MS
Excel spreadsheet software which will enable users to quickly adopt the system and
customise according to specific business requirements.
Statistical forecasting requires large data records (less than 500 records is considered
weak) for calculation. For KPPK this will require data from the agencies (and other
sources) to be extracted, transformed and loaded (ETL) to the KCommodities multi
dimensional database.
The ability to model the data and compute ROI, IRR and NPV using spreadsheets is a
builtin feature of the eIDA framework which will form the core foundation of K
Commodities. Commercial business modelling template can be added to interface
with the KCommodities through the spreadsheet feature.
Statistical forecasting software (such as SPSS or SAS) can be added to interface
with the KCommodities through OLAP or OLTP database connections. The ability to
ETL load the data from the agencies database applications is a built in feature of the
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eIDA framework which will form the core foundation of KCommodities. This will
require fast and secure network connection between the distributed databases.
KPPK will also need to expand the scope of data from the existing 34 datasets to
cover other data (such as transactional data) and other sources of data (such as from
the Department of Statistics, Meteorology Department, and other international
databases).
Most important, KPPK need to have a multidisciplinary statistician who is
competent in the statistical software and is knowledgeable of economics, business,
agriculture and commodities to identify what data is relevant, the significance of the
statistical output, and the potential impact / risk so that he can recommend the
appropriate decision.
5.3.2. Project Initiation
The project initiation phase primarily involves formally describing the project and
setting the guidelines. The project manager is expected to produce the Statement of
Work (SOW) document (which describes the project, scope, objectives, critical success
factors, work breakdown, and timeline) and the Project Charter (which will guide the
overall management of the project).
The ability to upload and share a repository of documents is built in feature of the e
IDA framework which will form the core foundation of KCommodities. A repository of
document templates can be created as a guide for the respective project managers.
5.3.3. Project Planning
The project planning phase is where the project manager will allocate the resources,
time, and cost according to the activities to produce an action plan for implementing
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the project. There are a number of specialised software that are able to do the
allocation, costing, gantt charting, and critical path method (CPM) analysis such as
Microsoft Project and Primavera.
The ability to enter and import the data for the planned activity items, dates and
costs is a builtin feature of the eIDA framework which will form the core foundation of
KCommodities.
5.3.4. Project Execution
The project execution phase is when the project is physically implemented. A project
manager will require a tool to monitor the progress of the project, as well as, control the
project. Monitoring is usually reported by comparing planned against actual activity
items, dates and costs. While control involves communicating, escalating and resolving
issues, and decisions to the appropriate person in charge.
There are two system applications that are used by the federal government to monitor
the physical implementation of projects. Jabatan Kerja Raya (JKR) uses SKALA, while
the Implementation Coordination Unit (ICU) uses SPP2. The upgraded version of
SPP2 was supposed to be able to extract data from SKALA.
The ability to update and report on planned against actual activity items, dates,
costs and status and the ability for project collaboration among the persons in
charge are built in features of the eIDA framework which will form the core foundation
of KCommodities.
5.3.5. Project Closure
The project closure phase is the last phase within the project management lifecycle. It
involves the project manager performing an assessment of the deliverables or
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completion criteria, and in some instances a benefit realisation report (BRR). The
project closure will normally compare the actual output against the SOW document
produced during the project initiation phase.
The ability to update and report on planned against actual activity output is a built in
feature of the eIDA framework which will form the core foundation of KCommodities.
5.4. KCommodities Information Framework
Based on the requirements set out in this chapter, the KCommodities system will
consist of 29 modules categorised as follows:
1. Executive Information System (EIS) Layer :
1. Personalised Dashboard
2. Dashboard eMail Function
3. Dataset Dashboard
2. Analysis Layer :
1. Charts
2. Tables
3. Mapping
4. Query
5. Document
6. News
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7. Directory Listing
8. Reporting Processing
9. Multidimension Online Analysis Processing
10. Query Processing
3. Multidimensional Dataset
1. Time dimension
2. Entity dimension
3. Project dimension
4. Item dimension
5. Geography dimension
6. Measure dimension
4. Data Capture Methods
1. Data Entry
2. File Upload
3. File Import
4. Data Load
5. RSS Aggregator
6. HTML Select
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7. Profile Update
5. System Administration
1. User Manager
2. Dataset Manager
3. Audit Log Statistics
KCommodities Information Framework
5.5. Information Sources
In order for KPPK to assess its achievement with respect to its four organisational
objectives it requires managing a lot of information that are distributed across many
databases and sources. Some are explicit but a lot of others are implicit.
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The 34 datasets from the 6 agencies in the current KCommodities scope will enable
KPPK to have a macro view of what has and is happening among its key agencies. It is
a good start for initiating a platform for business intelligence collaboration.
From this platform, KPPK can add other data sources to build its own knowledgebase
for achieving its mission of ensuring Malaysia's international competitiveness in the
commoditybased industries and their contribution towards national development. A
selection of the data sources that we recommend are:
1. Malaysian Department of Statistics : compiles and provides data on numerous
statistics on the Malaysian economy, industry, society, prices and employment.
These provide good indicators to identify trends and also structural changes in
the economy.
2. Malaysian Meteorology Department : publishes data on weather conditions in
Malaysia. This is usually a good factor for correlating agricultural production.
3. Malaysian Implementation Coordination Unit : collects data on the
implementation of federal projects throughout the country. A factor for
correlating fiscal expenditure.
4. Bank Negara Malaysia : publishes data on financial and monetary. Is a good
factor for correlating international trade and investment.
5. Food an Agriculture Organisation of the United Nations provides various global
statistics on agriculture production, consumption and trade. This is a good
source for international comparison.
6. United Nations International Merchandise Trade Statistics contains more than 1
billion trade records starting from 1962. A good source for indicators to identify
trends and structural changes in the world economy.
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7. United State Patent Office which contains data on patents registered in the
United States. A good source for measuring international competitiveness in
the sciences.
8. European Patent Office esp@cenet which contains data on patents registered
in Europe. A good source for measuring international competitiveness in the
sciences.
9. The Economist Intelligence Unit : provides a constant flow of analysis and
forecasts on more than 200 countries and six key industries.
10. Reuters : established provider of numerous global data, statistics and news, as
well as decisionsupport tools.
11. Google Finance provides news, price and trade volume data for North
American and Western Europe commodity markets.
12. Yahoo! Finance provides news, price and trade volume data for North
American and Western Europe commodity markets.
13. CNN Money provides news, price and trade volume data for North American
commodity markets.
There are various other country and commodity specific databases which may provide
more current data. There are also a lot more implicit information that has yet to be
organised and documented such as consumer values and market branding.
Managing these data will require a dedicated competitive intelligence (CI) unit with
the appropriate skills within the organisation. The objective of this CI unit is to provide a
model of explicit and implicit information that can best assist the organisational
strategic decision making. Their scope of work will be to gather, process and manage
the information through the KCommodities platform so that it can be disseminated
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and used across the organisation. The best example of a successful competitive
intelligence unit would be the Japan External Trade Organisation (JETRO) which assist
Japanese corporations on what to produce and export to foreign markets.
5.6. Implementation Strategy Roadmap
Based on this high level outline for the project module, there are two critical aspects
which are : what should be incorporated in the current Phase 2 of KCommodities; and
what should be the future direction of KCommodities.
Between these two aspects, consideration also need to be given to what is readily
available within the organisation and predictable as a platform for business intelligence
collaboration, while still providing the scalability to incorporate future needs.
Externalities which are presently outside of the organisation need to be ranked in terms
complexity and the impact on the timeframe.
Project Life Cyle KCommodities 2 Future Direction
Evaluation Modelling : Spreadsheet
interface to add business
modelling templates
Modelling : ETL to load data
from 34 datasets
Forecasting : Statistical
software to analyse and
predict future outcome
Forecasting : Extracting
transactional data and from
other external information
sources
Forecasting : Multi
disciplinary statistician for a
competitive intelligence unit
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Project Life Cyle KCommodities 2 Future Direction
Initiation Statement of Work :
Document and template
repository
Project Charter : Document
and template repository
Planning Schedule : Planned activity
items, dates and costs
Allocation : Project
management software that
are able to analyse critical
path on resource and activity
dependencies
Costing : Project
management software and
knowledgebase to specify
the bill of quantity (BQ) and
specifications
Execution Monitoring : Actual activity
items, dates, costs and
status
Control : Collaboration
among project group
members
Monitoring : Extracting data
from SKALA or SPP2
Closure Assessment : Actual activity
item output
Reporting : Document and
template repository
Assessment : Benefit
realisation analysis of socio
economic impact
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Based on the above matrix, there are three distinct milestones in terms of complexity
which are:
1. Where are we now? Which requires setting a common framework and
consolidating the data available from various internal sources so that the
organisation is aware of its strengths and weaknesses.
2. Where do we want to go? Which will require modelling and forecasting the
external competitive environment to uncover potential opportunities and threats
and setting the targets.
3. Are we there yet ? Which will require continuous evaluation of the optimal
strategies to achieve the organisational targets.
Implementation Strategy Roadmap
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The current Phase 2 of KCommodities need to focus on establishing the first level
complexity of knowing where the organisation stands and enforcing the monitoring and
control functions. Besides technology, it also need to address policies and procedures
on information sharing, creating awareness and interest among the people in the
organisation, and inculcating a culture of knowledge collaboration.
Between the second and third level of complexity, it will involve a lot with how the
organisation interacts with externalities. The second level can only start with a formal
competitive intelligence unit with clear objectives and scope of work. This unit will need
to continuously evaluate and recommend on optimal strategies on the third level of
complexity. This will lead the organisation towards the optimisation phase.
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6. HIGH LEVEL DOCUMENT OVERVIEW OF THE KCOMMODITIES SYSTEM
This part of the document presents the high level overview of the KCommodities
system based on the main findings highlighted in the previous chapters and outlines
the following:
1. Architecture for connecting the centralised KCommodities system to the
remote agency databases and users;
2. Functional modules that are necessary to capture the data, process the data,
provide an analysis of the information, present an executive view against
overall objectives and administer access to the system; and
3. Use case models to describe the interaction between the user and the
functional modules.
6.1. Architecture
The infrastructure architecture for KCommodities is based on the concept of a
centralised database that need to gather data from remote agency database which
also need to present the data to remote users. It will have the following components:
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NO COMPONENTS FUNCTIONS1 Database server to gather all the necessary data
2 Web application server to allow the analysis of the data through local area network (LAN) and internet
3 File server to receive data files from remote agency database / application through a virtual private network (VPN) connection
4 ETL (extracttransformload) server
to load the data files on the file server to the centralised database server
5 Mail Exchange server to push data / information to users' email
6 Reverse proxy server as a security against unauthorised access to the server
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VPN
File ETL
KCOMMODITIES : FASA
Mail Exchange
.
.
.
.
.
.
.
PC
K-Commodities Fasa 2 High-Level Document
6.2. Modules / Functions
KCommodities Phase 2 will have a total of 29 modules based on the requirements
highlighted in this report.
NO MODULE FUNCTIONS1 Dashboard – Personal Allows individual users to view and
personalize a collection of summary data
widgets from multiple datasets.
2 Dashboard Email Allows individual users to send a copy of the
dashboard to their email based on scheduled
time or defined event.
3 Dashboard – Dataset Allows users to view a collection of summary
data widgets from within a dataset.
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NO MODULE FUNCTIONS4 Charts Allows users to view reports in chart format
(bar, line, pie, radar, scatter or candlestick)
5 Tables Allows users to view reports in tabular format
and drill down to the report.6 Mapping Allows users to mark and view all location of
each commodity using Google maps.
7 Query Allows users to retrieve data from database
and display according to user selection.
8 Document The document repository function allows
electronic documents to be stored and shared
in the system by users.
9 News Allows users to link the news using RSS
Feed.
10 Directory Allows users to display a directory listing of
records (such as license holders)
11 Analysis – Reporting Allows user to print or export a report to a
spreadsheet format or viewed in graphical
format.
12 Analysis – MOLAP Allows analysis of data through the use of a
multidimensional data model. Users will be
able to view different aspects or facets of data
aggregate (dataset).
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NO MODULE FUNCTIONS13 Analysis – Query Allows users to query data from different
section and subsection and displayed
according to their selection.
14 Multi Dimension Dataset
– Time
Allows data to be categorized in time series
form (time, date, month, quarter, halfyear,
year).
15 Multi Dimension Dataset
– Entity
Allows data to be categorized in multitier
organization format (organization, branch,
department, unit).
16 Multi Dimension Dataset
– Project
Allows user to keep project data and view the
data based on portfolio, project, phase and
activity.17 Multi Dimension Dataset
– Item
Allows data of any item to keep in system
(type, group, item, subitem).
18 Multi Dimension Dataset
– Geography
All geographical dataset will be kept in the
system.
19 Multi Dimension Dataset
– Measure
Allows measurement will be kept in the
system (type, measure, units).
20 Data Entry Allows user to add, edit and delete data.
21 File Upload Allows user to upload documents and store in
the system.
22 File Import Allows users to import data from external
spreadsheet using predefined templates.
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NO MODULE FUNCTIONS23 Data Load Allows application to load data from agencies
database directly to the system database.
24 RSS Aggregate Allows application to syndicate web content
such as related news to be integrated with the
system.
25 HTML Select Allows application to read spreadsheet files
contain unique cell references in the HTML
class tag for each data value and insert into
the database.
26 Profile Allows users to create, update and delete
directory records (such as license holders).
27 Access Control – User
Manager
Allows system administrator to add, edit,
delete and grant access permission for
system users. Access to this function is
restricted to system administrator and section
administrator.
28 Access Control – Dataset
Manager
Allows section administrator and system
administrator to add, edit, delete and grant
access permission for users to the dataset.
29 Access Control – Log
Statistics
Application will log and record the usage and
user activities in real time.
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6.3. Use Case
6.3.1. Types of Users
The table below summarizes the different type of users:
Type of User Roles
Entity Users To perform data entry and view reports only for a
specified dataset and organisation.
Users To perform data entry and view reports according for a
specified dataset.
Executive User To view reports.
Section Administrator To manage users and datasets within his organisation
only.
System Administrator To manage users and datasets for overall system and can
access all organisation’s datasets.
Type of User
6.3.2. Use Case Diagram
This section lists use cases or scenarios from the usecase model which depict
significant and central functionality of the final system.
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Data
Document
Report
Dashboard
USER(KPPK)
EXECUTIVE USER(KPPK)
ENTITY USERS(Agencies)
* limit to their datasets only
Users
Datasets
Database
SECTION ADMIN(Agencies)
SYSTEM ADMIN(KPPK)
Add / edit / view / comment
view /
commen
t
Add / edit / view / comment
Use Case Diagram
6.4. Flowchart
Please refer to flowchart below for detail function and roles of each user:
6.4.1. Type of User : USER
User at KPPK can perform the task of data entry and view their personal dashboard
which is contain their datasets and can manage their own dashboard.
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START
Login System
Personal Dashboard
Select Menu
Select Widget
Display Dataset
Select Function
Report MOLAP QueryEntry
Chart Table Map
Time Entity Project I tem Geography Measure
Query
Data Entry
File Upload
File Import Data Load
RSS Aggregate
HTML Select
User’s Flowchart
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6.4.2. Type of User : ENTITY USER
Entity users are from KPPK’s agencies which are MPOB, MRB, MTIB, MPB, MCB and
LTN. They only can manage their dataset only, but can view datasets from other
agencies. Entity users can keyin data for their agency only.
START
Login System
Personal Dashboard
Select Menu
Select Widget
Display Dataset
Select Function
Report MOLAP QueryEntry
Chart Table Map
Time Entity Project I tem Geography Measure
Query
Data Entry
File Upload
File Import
Data LoadRSS
AggregateHTML Select
Entity User’s Flowchart
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6.4.3. Type of User : EXECUTIVE USER
Top level management are categorised as Executive User. They can view report,
query the data and manage their own dashboard.
Executive User’s Flowchart
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START
Login System
Personal Dashboard
Select Menu
Select Widget
Display Dataset
Select Function
Report MOLAP Query
Chart Table Map Query
Time Entity Project I tem Geography Measure
K-Commodities Fasa 2 High-Level Document
6.4.4. Type of User : SYSTEM ADMINISTRATOR
System administrator will manage all users and dataset for overall system and can
access all agencies datasets.
START
Login System
Personal Dashboard
Select Menu
Select Widget
Display Dataset
Select Function
Report MOLAP Query Entry Access Control
Chart Table Map Query
Data Entry
File Upload
File Import
Data Load RSS Aggregate
HTML Select
User Manager
Dataset Manager
Log Statistics
Time Entity Project I tem Geography Measure
System Administrator’s Flowchart
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6.4.5. Type of User : SECTION ADMINISTRATOR
Section administrators are from KPPK’s agencies. They only can manage the users
and datasets from their agencies only .
START
Login System
Personal Dashboard
Select Menu
Select Widget
Display Dataset
Select Function
Report MOLAP Query Entry Access Control
Chart Table Map Query
Data Entry
File Upload
File Import
Data LoadRSS
AggregateHTML Select
User Manager
Dataset Manager
Log Statistics
Time Entity Project I tem Geography Measure
Section Administrator’s Flowchart
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