Applying GSBPM in the National Enterprise- Wide Statistical Systems (NEWSS ): Monthly Manufacturing...

Applying GSBPM in the National Enterprise-Wide Statistical Systems (NEWSS ):

Monthly Manufacturing Survey

DEPARTMENT OF STATISTICS MALAYSIA1st October 2014

Modernization Working Group(MSIS 2010)

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Monthly Manufacturing Survey (MM)

Objectives :i. Provide data on the latest trends in the principal statistics of the

manufacturing sector, specifically on sales value, number of employees, salaries & wages and the main products manufactured, hence assisting users in policy formulation and decision making.

ii. Data obtained are used for the compilation of Industrial Production Index (IPI) and Monthly Manufacturing Statistics.

iii. IPI as an input for the compilation of quarterly Gross Domestic Product (GDP) and monthly Leading, Coincident and Lagging Indexes.

iv. To assist economists, academicians and public users in economic analysis.

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SCOPE AND COVERAGE

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• Geographically, cover whole of Malaysia.

• 120 industries out of a total of 197 industries covered

• Of the 120 industries covered, 102 are used in the computation of the Index of Industrial Production.

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APre-Collection

BCollection

CProcessing

DAnalysis

EDissemination

StatisticalEnvironment

CProcessingAnalysis

D CProcessingAnalysis

A PLATFORM

The technically capable

The professionally informed Everyone else

MM Statistical Environment (NEWSS)

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Integrated Statistical Systems FrameworkIntegrated Statistical Systems Framework

Source : DOSM

NEWSS VisionNEWSS Vision

NEWSS INTERFACE

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Mapping to GSBPM

Processing

Collection

Pre-collection

Dissemination

Analysis

Monitoring & Quality Assurance Evaluation

Archive

Disseminate

Analyse

Process

Collect

Specify Needs

Build

Design

Processing

Collection

Pre-collection

Dissemination

Analysis

Monitoring & Quality Assurance

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1 Profil Banci/Penyiasatan(Census/Surveys Profile)

Access project profile details for the planned/ implementing/ implemented census/surveys with their details activities.

2 Pra Penggumpulan Data (Pre Data Collection)

Access pre-collection details i.e. scope of coverage for establishment census/surveys and sampling details.

3 Pengumpulan Data(Data Collection)

Access collection process details including the assignment of officers and field enumerator to sample case, capturing the operational control and operational visit information.

4 Prosesan Data(Data Processing)

Capturing the census/survey data, secondary data, process the batch file with census/survey data from offline data entry module.

Access the link to enable (first installation) the offline data entry module on local workstation.

Configure the data cut off date for economy surveys and survey data cut off period for monthly manufacturing survey.

For surveys with probability sampling, capturing the related data required for weighted data processing and processing of the weighted data.

Modules in NEWSS

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5 Penyebaran(Dissemination)

Manage product, catalog, advance release calendar, customized request, data request, customer services, external user account, subscription and feedback.

Track the total transactions, delivery on hardcopy/CDs, notification recipients.

Log the email notifications sent on subscription expiry and catalog release.

Maintain the related reference of products, delivery zone and cost table, currency exchange rate, service charge and government service tax.

6 Pensampelan(Sampling)

Perform the details process on sample generation.

Perform the selection of living quarter for field work.

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Rangka Enterpris/Pertubuhan(Enterprise / Establishment Frame)

Maintain the enterprise/establishment frame, screening information, other agencies information (ROC, ROB, CIDB, KWSP).

Manage the duplication of enterprise/establishment and duration of the new establishment.

Modules in NEWSS

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8 Laporan(Report) Access to the ISSF reports and KMS reports.

9 Kod dan Klasifikasi(Code & Classification)

Maintain the standard code and classification for social demographic and economy.

10 Metadata Maintain data dictionary that describe the data available in DOSM Central Repository and Other Agencies Database.

Modules in NEWSS

11 Business Intelligence

Generate the review tables for SSE and Monthly Manufacturing survey.

Generate the tabulation for publication on SSE, MM and PTB.

Provide the dynamic analysis capabilities on the data available, used to serve the data request.

2 Design

3 Build

4 Collect

5 Process

6 Analyse

7 Disseminate

1 Specify

Needs

3.5 Test statistical

business process

3.4Test

productionsystem

3.3

Configureworkflows

3.2 Build or enhance

processcomponents

3.1Build datacollection instrument

1.6

Preparebusiness case

1.5

Check dataavailability

1.3Establish

outputobjectives

1.2

Consult andconfirm needs

1.1Determineneeds for

information

2.6Design production

systems andworkflow

2.5Design statistical

processing methodology

2.4Design frame and sample

methodology

2.3Design datacollection

methodology

2.2Design variable

descriptions

2.1

Designoutputs

4.4

Finalize collection

4.3

Run collection

4.2

Set up collection

4.1

Select sample

5.1

Integrate data

5.2

Classifyand code

5.3 Review, validate and edit

5.4

Impute

5.5 Derive new

variables andstatistical units

5.6

Calculateweights

5.7

Calculate aggregates

6.1

Prepare draft outputs

6.2

Validate outputs

6.3

Scrutinizeand explain

6.4Apply

disclosure control

6.5

Finalize outputs

7.5Manage

usersupport

7.4Promote

disseminationproducts

7.3 Manage release of dissemination

products

7.2Produce


7.1

Update output systems

8 Archive

9 Evaluate

8.2Manage archive

repository

8.1

Define archive rules

8.3Preserve dataand associate

metadata

8.4 Dispose of dataand associated

metadata

9.1Gather

evaluationinputs

9.2

Conduct evaluation

9.3

Agree action plan

1.4

Identify concepts

3.6 Finalize

productionsystem

5.8

Finalizedata files

Generic Statistical Business Process Model

Version 4.0 – 2009

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1 Specify

Needs

1.6

Preparebusiness case

1.5

Check dataavailability

1.3Establish

outputobjectives

1.2

Consult andconfirm needs

1.1Determineneeds for

information

1.4

Identify concepts

Generic Statistical Business Process Model Version 4.0 – 2009

Needs for information identified and determined by Subject Matter Division (SMD) concerned, align with international practices

Users and stakeholders requirement taken into account. Main stakeholders for MM statistics – MITI, EPU, SMIDEC, MIDA, BNM

Concepts identified are internationally comparable, revised when necessary – to ensure align with international standards. These include definitions, codes (MCPA, MSIC) and manual (e.g. SNA)

Checking and assessment on data gaps and availability done regularly based on user requirements

Business case prepared align with the monitoring and quality assurance policy (Malaysia Standard (MS) of ISO 9001:2008)

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2 Design

2.6Design production

systems andworkflow

2.5Design statistical

processing methodology

2.4Design frame and sample

methodology

2.3Design datacollection

methodology

2.2Design variable

descriptions

2.1

Designoutputs


Detailed design of the statistical outputs to be produced - Functional Design Specification (FDS).

Variable descriptions – determined by SMD, follow international standards

Sub-processes 2.3 - identified and determined by SMD – questionnaire (face to face interview) and e-survey (online data entry)

Population of interest identified and specified by Methodology Management Division. Frame sources from business registers, census and sample surveys.

Sub-process 2.5 – specifications prepared by SMD

Production systems and workflow – implemented and documented align with MS ISO 9001:2008 requirement

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MM1 : Pre-Collection

MM PROCESS FLOW

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(Cont’d)

MM PROCESS FLOW

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(Cont’d)

MM PROCESS FLOW

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MM4 : Collection & Processing

MM PROCESS FLOW

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(cont’d)

MM PROCESS FLOW

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(cont’d)

MM PROCESS FLOW

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(cont’d)

MM PROCESS FLOW

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MM8 : Analysis &

Dissemination

MM PROCESS FLOW

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3 Build

3.5 Test statistical

business process

3.4Test

productionsystem

3.3

Configureworkflows

3.2 Build or enhance

processcomponents

3.1Build datacollection instrument

3.6 Finalize

productionsystem


Data collection instrument built based on specifications created during phase 2 (Design). Multiple modes of data collection used – questionnaire : face to face interview, facsimile, e-mail and e-survey.

Enhancement of existing system occurs in NEWSS Phase III, includes Force Accept Report, Question 2 Layout display, time out session for e-Survey & Flow Registration for e-Survey. Workflows configured include all activities – from data collection, right through to archiving the final statistical outputs.

Testing of computer systems and tools conducted thoroughly to ensure the interactions between modules involved works as a coherent set of modules.

Comprehensive documentation (URS, BP, FDS) accessible easily by users through intranet.

Training on how to operate the system conducted for all users (internal and external).

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4 Collect

4.4

Finalize collection

4.3

Run collection

4.2

Set up collection

4.1

Select sample


Sample selected from business frame established in sub process 2.4 (Design frame and sample methodology). Compliance with MS ISO procedures : Technical Instructions (JPSP-AK-PP-02) Strategy, planning and training activities prepared and conducted in compliance with MS ISO procedures (JPSP-PK-04 & JPSP-AK-SPB-01)

Data Collection monitored through Operational Information Control (MKO) module where response rate and data collection mode reported at real time basis. Records of when and how providers were contacted are also included.

Data collected stored automatically in the system for further processing phase. However, metadata are stored in different module. Data entry either done manually or via e- survey.

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5 - Process

5.1

Integrate data

5.2

Classifyand code


5.4

Impute

5.5 Derive new


5.6

Calculateweights

5.7


5.8

Finalizedata files


Data collected via paper questionnaires and e-survey are matched and linked automatically in the system. The process takes place at real time based, regardless of whether the data collection has been completed or not.

Input data classified and coded within the system, where coding routines done according to a pre-determined classification scheme prepared by the SMD.

Errors and data discrepancies (outliers, non-response & miscoding) identified and validated within the system, run iteratively based on predefined edit rules.

Imputation routines done automatically by the system for non-response cases, according to pre-defined methods. Imputed cases are flagged by the system.

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5 – Process (cont’d)

5.1

Integrate data

5.2

Classifyand code


5.4

Impute

5.5 Derive new


5.6

Calculateweights

5.7


5.8

Finalizedata files


Arithmetic formulae applied in the system to derive new variables from variables already present in the dataset, i.e. principal statistics (Sales Value of Own Manufactured Products, Number of Employees and Salaries & Wages)

The calculation method of weights and aggregates are provided in the system in order to “gross-up” sample survey results to be representative of the target population.

Final data files stored automatically in the system and ready to be used as input for Phase 6 – Analyze.

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6 Analyse

6.1

Prepare draft outputs

6.2

Validate outputs

6.3

Scrutinizeand explain

6.4Apply

disclosure control

6.5

Finalize outputs


Data collected transformed into statistical outputs, to indicate the latest trends of manufacturing sector and compiled for the production of Industrial Production Index. SMD responsible in validating the outputs produced, in accordance with the Quality Check procedures described in the MS ISO 9001:2008 quality document (JPSP-06-AK-SPB- 01 & JPSP-06-AK-SPB-02).

Improvement in carrying out in-depth statistical analysis is an essential aspect to be taken into account especially in assessing how well the statistics reflect the initial expectations. Data Dissemination Policy and Statistics Act established for the

purpose of protecting the confidentiality of the data and metadata released.

Output finalized after the completion of relevant processes (consistency checks, determine level of release, collating supporting information, etc) and reached the required quality level.

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7 Disseminate

7.5Manage

usersupport

7.4Promote


7.3 Manage release of dissemination

products

7.2Produce


7.1

Update output systems


System updated regularly parallel to the completion of data processing at time stipulated by SMD. Data and metadata are stored automatically in the system for dissemination purposes.

In the case of MM Survey, products are disseminated in the forms of printed publications, tables and also charts in the web site (Statistical Release) .

Press release of the products uploaded to the website at the embargo time and also on stipulated date in the Advance Release Calendar (compliant to SDDS).

Promotion of the products is done through website (Latest Statistical Release).

Customer Queries and Satisfactions are monitored based on quality assurance document (JPPS-PK-07) to ensure responses are provided within agreed deadlines and follow specified workflow.

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Start

Receive complaints

Record complaints

Refer toPB/TP/PP (BKKP)

P/TP/PP (JPN)

Justified?

Examine complaints

Close case and filed

Forward to PYB(SKPA)

Investigate

Prepare report and response draft

AB

No

Yes

End

Forward toPB/TP/PP (BKKP)

Agreed?

PYB receive report draft

Inform the complainant

Correction

Record

A

B

No

Yes

I. Handling of Complaints Workflow

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II. Handling of Customer Satisfaction Workflow Start

Received completed form from customer

Analyze and prepare report

Forward toPB/WP/PB (BKKP)

Agreed?

Send the form to main user

Correction

Receive result/recommendation and

inform P(UKP)

Take further action

Present in MKSP

No

Yes

End

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8 Archive

8.2Manage archive

repository

8.1

Define archive rules

8.3Preserve dataand associate

metadata

8.4 Dispose of dataand associated

metadata


Standard archive rules need to be determined and established in order to ensure data and metadata resulting from the statistical business process are archived systematically and efficiently.

Archive repository for data and metadata is currently not available. Data and metadata are stored in the system, but in two (2) different modules.

Sub-process 8.3 and 8.4 are not yet fully implemented for Monthly Manufacturing Survey data.

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9 Evaluate

9.1Gather

evaluationinputs

9.2

Conduct evaluation

9.3

Agree action plan


Evaluation inputs include feedback from users , staff suggestions and also internal audit. which is conducted periodically by internal auditor and SIRIM (Standards & Industrial Research Institute of Malaysia).

Audit Committee was formed to monitor the quality of all statistical business process involved in producing the outputs.

Internal audit conducted periodically in accordance to Quality Procedures (JPPS-PK-06 & JPPS-06-AK-01). It involves the SMD concerned and all state offices.

The evaluation report will become the basis for decision– making power to form and agree an action plan. Corrective action and improvement plan must be included into consideration as a mechanism of monitoring the quality of the outputs.

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CHALLENGES

1. Metadata management

i. database environmentii. stored together with dataiii. availability based on users’ needsiv. exchange and use – infrastructure

2. Strengthening and empowerment of statisticians skills and ability in scrutinizing and analyzing the statistics produced.

3. Needs to define and determine archive rules/procedures for statistical data and metadata resulting from a statistical business process.

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CONCLUSIONS

Department of Statistics Malaysia is committed to standardize itsproduction environment and will continue to expand and improve its common toolbox based on the needs of the surveys carried out.

Main focus for development in the next coming years will be on how information will flow and be transformed through the different stages of GSBPM.

Higher level officers from all SMDs will be included in the initiatives of modernization of statistical products and services, particularly in adopting the GSBPM in every statistical business process

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Thank YouTerima Kasih

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