process improvements andrew kochen, hmrc september 2008
TRANSCRIPT
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Process Improvements Andrew Kochen, HMRC
September 2008
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Context
No funding in the short term for large IT system improvements• Looked at how to make other improvements
Reorganised the Trade Statistics area, with a temporary increase of staff in statistical team to maintain the quality of the data but leading to an overall reduced staff
Using a mix of small scale, largely technical changes to lead to overall improvement in the data
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•General principles
• Aim to get data in ‘correct and on time’ (reduces corrections to be made later in process)
• Risk targeting – focussing effort on data items that have most impact on the trade statistics
• ‘Lean’ – cutting out actions that do not add (much) value to the overall process
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Technical improvements
• Improved front-end tools for more efficient prioritisation of credibility interventions
• New SAS-based credibility parameter software, allowing faster update (e.g. reflecting price fluctuations) and more efficient application
• Introduction of an integrated risk database, providing risk assessment for each line of trade (comcode, trade, country), linked to and highlighting priorities via the front- end credibility tool
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The scatter graph shows the data and the regression line for the model. The parameters for this model are as follows: A = 5.173 B = 0.328 S = 0.717
Rsq = 0.2656
COMCODE: 34060011 Candles
LNQTY1
14121086420
LNV
ALU
E9.5
9.0
8.5
8.0
7.5
7.0
6.5
6.0
5.5
5.0
4.5
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Trader Contact
• Self-service education increased for EU data providers (Intrastat traders) via web and phone
• Use of email alerts to notify data providers of changes, recurring errors, due date, etc
• Improvements to e-declaration systems, leading to increased take up
• Desk based assurance of data provider declarations (targeting and phoning as opposed to visiting)
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Joint Working between Statistics teams and Operations (Processing)
• Greater integration of data analysts and statisticians into data operations work (co-location, Risk and Quality groups, etc.)
• Evaluation / measurement of all process changes, improvements, targeting to understand impact, replicate, stop, etc
• Improved knowledge and understanding of the business by staff members, improved documentation and archiving. Supports greater staff flexibility, utilisation, motivation and business contingency
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• Targeting of data provider risk as opposed to general assurance
• Use of improved management information and data analysis to prioritise manual interventions to the most significant data providers
• Improved reporting of multi-item data provider errors on one output, reducing confusion and allowing consolidated contact calls to rectify, reducing provider burden, etc
Joint Working between Statistics teams and Operations - continued
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• Changes to our estimation methodologies • Estimation of Non response• Partial response • Non coverage (Below Threshold Trade)
• Improving the estimates for the effect of Missing Trader Intra-Community Trade (missing declarations) on trade in goods
• The requirements for a new IT System
In progress
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Conclusions
• The improvements have shown the larger team now has a common goal and direction
• There is a greater working together with understanding of the aims and problems of both sides of the business
• The quality of the data is being maintained and compliance is improving
• While large scale investment in IT is still required, this series of small changes within our existing budget have lead to data quality improvements
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Questions
1. How do trade statisticians measure the overall quality of the data and error rates?
2. How can we quantify improvements and assess the impact on data quality?