1 MODERNIZATION OF BELARUSIAN STATISTICS _________________________________________________ IMPLEMENTATION OF THE PROCESS APPROACH IN ORGANIZING THE STATISTICAL PRODUCTION Irina Kostevich National Statistical Committee of the Republic of Belarus June 2014, Nizhny Novgorod, Russia

PROBLEM System of “chimneys” Industry Statistics Labour Statistics Price Statistics Trade Statistics

10-12 June 2014, Nizhny Novgorod, Russia BACKGROUND ON THE NATIONAL MODEL BUILDING CHANGING users requirements REDUCTION in number of employees statistical system STRUCTURE OPTIMIZATION NEED FOR STANDARDIZATION OF STATISTICAL PROCESSES CREATION OF A PROCESS-ORIENTED MODEL of statistical production

4 Everything that is STANDARDIZED, could be MEASURABLE, and consequently, MANAGED AND EXECUTED

EMPHASES building process-oriented model of statistical activity documentation and standardization of all statistical production processes defining process managers commitment to quality of products and processes June 2014, Nizhny Novgorod, Russia

PROCESS-ORIENTED MODEL is necessary for everyone! For specialist FUNCTIONS TRANSPARENCY AND CLARITY For manager QUALITY MANAGEMENT TOOLS PLAN, MEASURE,ANALYZE, IMPROVE,REALLOCATE

February 2014 – pilot surveys description Labour statistics Industry Statistics June 2014, Nizhny Novgorod, Russia Use the GSBPM 5.0 to describe the existing statistical production processes

Results: gaps Identification of gaps in the existing processes June 2014, Nizhny Novgorod, Russia Lack Lack of necessary documentation unsettled Existence of unsettled processes

9 Identification of needs Design Build Collection Process Analyse Deliver and dissemination Data archiving Data protection National Statistical Production Model Evaluation Evaluation

FEATURES OF THE BELARUSIAN STATISTICAL PRODUCTION PROCESS-ORIENTED MODEL 4. Collection 5. Process 6. Analyze 7. Deliver and dissemination 8. Data protection 9. Data archiving

PROCESS-ORIENTED MODEL OF STATISTICAL PRODUCTION OF BELARUS 1. Specify needs 2. Develop and design 3. Build 4. Collect 5. Process 6. Analyze 7. Deliver and Disseminate 10. Evaluate 2.1. specify composition of statistical indicators, develop the methodology of their formation 3.1. build primary statistical data collection tools 5.1. integrate data 6.1.prepare preliminary results (calculate additional indicators) 7.1. produce statistical publications gather evaluation inputs 1.2. establish objectives 2.2. specify the list of statistical classifications and nomenclatures 3.2. build or enhance data processing technology 4.2. acquire administrative data 6.2. control and interpret the results 7.2. update geographical database, BMB, BM 1.3. check data availability 2.3. design aggregate limits and sampling methodology 3.3. build or enhance software and hardware facilities, test them conduct evaluation 4.3. finalize primary data collection (input, code, completeness) 5.3. calculate weights 7.3. manage official statistical data dissemination develop and agree further action plan 1.4. develop grounding for implementation of new statistical monitoring 2.4. develop and test statistical tools 3.4. build tools for dissemination of official statistical information 6.3. disclosure control 7.4. promote disseminated products 5.4. derive basic aggregated data 2.5. approve statistical tools 5.5. control aggregated data 6.5. finalize and approve outputs 7.5. manage customer queries 2.6. design and approve technical process of statistical production 3.5. finalize production system 5.2. control and revise data 4.1. collect primary statistical data 1.1. Analyze and specify the users’ needs 8. Data protection 9. Data archiving STATISTICAL PRODUCTION QUALITY MANAGEMENT 1.5. define competence for organizing and carrying out statistical monitoring

PROCESS APPROACH PROCESSES Defining MANAGER – THE PROCESS HOST Building of PROCESS MANAGERS TEAM SURVEYS Defining MANAGER FOR SURVEY CONDUCTING Building of SURVEY MANAGERS TEAM June 2014, Nizhny Novgorod, Russia

13 INSTITUTIONAL LEVEL QUALITY MANAGER PROCESS MANAGER INDUSTRIAL LEVEL INDUSTRIAL QUALITY MANAGER LEVEL OF SURVEYS MANAGER FOR SURVEY CONDUCTING

14 REGULATIONS for a process ( documented description of every process ) SURVEY Process model Guidelines on process model (Regulations’ handbook)

SUPPOSED EFFICIENCY June 2014, Nizhny Novgorod, Russia DEFINITION of clear responsibility limits of managers and specialists OPTIMIZATION of labor force and costs DEFINITION of problematic issues and high cost processes FORECASTING of performance results

QUALITY MANAGEMENT SYSTEM June 2014, Nizhny Novgorod, Russia  Quality management of resources and processes, building efficient production  Good guide for future steps of development  Guarantee for increasing confidence in statistics  Organization image

Process-oriented model of statistical production and quality management system is: PURPOSE – TO IMPLEMENT IT AND MAKE IT WORK! AN INNOVATION in Belarusian statistics A MODEL, which can dramatically increase performance efficiency, data and services quality our GROWTH MODEL

18 MODERN MANAGEMENT in STATISTICS TO SATISFY A USER WITH HIGH QUALITY OF DATA and SERVICES TO ENSURE THE BUDGETARY FUNDS AN EFFICIENT USE TO ENSURE OPTIMAL RESPONSE BURDEN TO ENSURE THE HUMAN RESOURCES AN EFFICIENT USE RESULT, SATISFACTION AND INTEREST

Вопросы? THANK YOU FOR YOUR ATTENTION June 2014, Nizhny Novgorod, Russia