K-COMMODITIES 2.0 KEMENTERIAN PERUSAHAAN …software.krimnet.com/files/KComm_HighLevelDoc_20090529.pdfK-Commodities Fasa 2 High-Level Document 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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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



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



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.



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



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:



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).



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



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




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)


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





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.


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).



No. Issue Proposed Functional Requirements

1 Most information required are received through phone, letters, fax and email.




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.




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 Download feature to download or print the selected textual information in document format.




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



◦ Log Statistics EMail Alert function to remind the officer in charge if the information not updated within the time limit.




◦ 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.



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.


◦ 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.



7 Requirement to obtain information to perform comparison between





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



◦ 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



◦ Dataset Project dimension to organise the data according to RMK Plan and




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.



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.



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.





entered.



notification.

1.6 Production Data Data should be formatted to make


easily.

Add filter to view the data.



No Menu Description Comment1.7 MAIN MENU

– Price Price Input User should be allowed to input

the data by batch.





entered.



notification.

1.8 Price Data Data should be formatted to make


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.





entered.



No Menu Description Comment1.12 Export Data Data should be formatted to make


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.





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



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



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.



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



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



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



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



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




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


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




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



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


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





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


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



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 /



Bil KEPERLUAN KPPK MRBContinent

Method XLS


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


Time Series YearlyEntity Yes



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


Time Series QuarterlyEntity YesProject NoGeography Region Method HTML

14 Data pesaing Y / T / TB(negara lain) Data Source


15 Data pesaing Y / T / TB(komoditi/produk lain) Data Source


16 MoU Y / T / TBData SourceTime SeriesEntityProject



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



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


26 Maklumat AP Y / T / TBData Source Time Series Entity Project Geography



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



Bil KEPERLUAN KPPK MRBGeography No Method Manually


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


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



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 /



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


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




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




12 Bilangan R&D Y / T / TB Yesdihasilkan atau Data Source Manuallydikomersilkan Time Series Yearly

Entity NoProject YesGeography Country / RegionMethod Manually


Time Series DailyEntity YesProject No


Method System Data

14 Data pesaing Y / T / TB Yes(negara lain) Data Source External / Manually

/ DatabaseTime Series YearlyEntity YesProject No



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



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



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



MethodUpload / System Data

29 Curing barn Y / T / TB Not related

change to Data Source facilities provided Time Series



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


Time Series DailyEntity YesProject No


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 /



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


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



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




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



Time Series YearlyEntity Project GeographyMethod System Data



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


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



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



Bil KEPERLUAN KPPK LTN Entity Project Geography Method

25 Maklumat negara Y / T / TB Yeslain (profil) Data Source Manually


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


29 Curing barn Y / T / TB Yeschange to Data Source Manuallyfacilities provided(perkhidmatan yg disediakan)

Time Series Yearly



Bil KEPERLUAN KPPK LTN Entity Project No Geography District Method CSV / XLS


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


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



Bil KEPERLUAN KPPK LTNCost of Production Project No Geography District Method CSV / XLS


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



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



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



Time SeriesQuarterly / Half year / Yearly

Entity Project Geography StateMethod CSV / XLS


Data SourceManually

Time Series Entity



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


Time Series YearlyEntity Project



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



Bil KEPERLUAN KPPK MCB


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


29 Curing barn Y / T / TB Not relatedchange to Data Source facilities provided(perkhidmatan yg disediakan)

Time Series




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 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




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



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


Time Series YearlyEntity Project Geography StateMethod System Data

8 Guna tenaga Y / T / TB Not related(asing) Data Source


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



Bil KEPERLUAN KPPK MPB(mekanisasi/otomasi) Time Series Yearly

EntityYes (Kilang / Ladang)

Project No Geography StateMethod System Data


Time Series YearlyEntity Project Geography DistrictMethod CSV

12 Bilangan R&D Y / T / TBdihasilkan atau Data Sourcedikomersilkan Time Series



Time Series YearlyEntity Project Geography DistrictMethod System Data


Data SourceDatabase

Time Series YearlyEntity Project Geography CountryMethod System Data

15 Data pesaing Y / T / TB Yes(komoditi/produk lain) Data Source



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


Time Series

Entity Project Geography StateMethod Manually

20 Bilangan Kilang Y / T / TB YesData Source Manually keyin



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


Time Series YesEntity



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



Y / T / TB Not related

* Change to Data Source Maklumat Integrasi Time Series



Bil KEPERLUAN KPPK MPB Entity Project Geography Method

31 Maklumat perjawatan Y / T / TBagensi Data Source



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




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.



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)



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:



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.



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



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



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



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


http://www.neuroxl.com/white_paper_neural_networks.htm

http://www.ers.usda.gov/Data/PriceForecast/

http://www.finance30.com/forum/topic/show?id=1987892%3ATopic%3A15990

http://www.finance30.com/forum/topic/show?id=1987892%3ATopic%3A15990


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


http://www.palisade.com/stattools/


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



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



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



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



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.



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.



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


mailto:esp@cenet


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



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



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



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.



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:



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


VPN

File ETL

KCOMMODITIES : FASA

Mail Exchange

.

.

.

.

.

.

.

PC


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.



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).



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).


– Entity

Allows data to be categorized in multitier

organization format (organization, branch,

department, unit).


– 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).


– Geography

All geographical dataset will be kept in the

system.


– 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.



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.



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.



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.



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



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


Query

Data Entry

File Upload

File Import

Data LoadRSS

AggregateHTML Select

Entity User’s Flowchart



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


START

Login System

Personal Dashboard

Select Menu

Select Widget

Display Dataset

Select Function

Report MOLAP Query

Chart Table Map Query



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


Data Entry

File Upload

File Import

Data Load RSS Aggregate

HTML Select

User Manager

Dataset Manager

Log Statistics


System Administrator’s Flowchart



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


Data Entry

File Upload

File Import

Data LoadRSS

AggregateHTML Select

User Manager

Dataset Manager

Log Statistics


Section Administrator’s Flowchart



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