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This article outlines a framework for formal description, justification and evaluation in development of architectures for large-scale statistical production systems. Following an introduction of the main components of the framework, we consider four related issues: (1) Use of some simple schematic models for survey quality, cost, risk, and stakeholder utility to outline several groups of questions that may inform decisions on system design and architecture. (2) Integration of system architecture with models for total survey quality (TSQ) and adaptive total design (ATD). (3) Possible use of concepts from the Generic Statistical Business Process Model (GSBPM) and the Generic Statistical Information Model (GSIM). (4) The role of governance processes in the practical implementation of these ideas.

. Taylor, C., G. James, and P. Pring. 2011. “The Development of the Monthly Business Survey.” Economic and Labour Market Review 5: 95-103. UNECE. 2013. Generic Statistical Business Process Model GSBPM (Version 5.0). Geneva: UNECE. Available at:þv5.0 (accessed 10 November 2015). Van den Brakel, J. 2010. “Sampling and Estimation Techniques for the Implementation of New Classification Systems: the Change-Over from NACE Rev. 1.1 to NACE Rev. 2 in Business Surveys.” Survey Research Methods 4: 103-119. Doi: http

approach, recent years have seen an active international collaboration regarding the foundations of production of statistics. Common conceptual frameworks, like the Generic Statistical Business Process Model (GSBPM) and Generic Statistical Information Model (GSIM), are the results of such international initiatives. In addition, there is a wide range of q Statistics Sweden 1 The terms that we are using in this introduction are well explicated in the contributions to this special issue, which is the reason we, for brevity, refrain from providing references here. Journal of

August 2014). UNECE. 2013a. Generic Statistical Information Model (GSIM), version 1.1. Available at:¼59703371 (accessed August 2014). UNECE. 2013b. Common Statistical Production Architecture (CSPA), version 1.0. Available at:þv1.0 (accessed August 2014). UNECE. 2013c. Generic Statistical Business Process Model (GSBPM), version 5.0. Available at:þGenericþStatisticalþBusinessþProcessþModel (accessed