Spreadsheet Implementations for Linking Multi-Level Contribution Margin Accounting with Multi-Level Fixed-Charge Problems
Description
This site provides the data and spreadsheet implementations for linking multi-level contribution margin accounting as a subsystem in cost accounting with several versions of a multi-level fixed-charge problem (MLFCP), the latter based on the optimization approach in operations research. For the data, plausible fictitious values have been assumed taking into consideration the calculation principles in cost accounting where applicable. They include resource-related data, market-related data, and data from cost accounting. The deterministic version of the data represents the starting point. On this basis, the stochastic/robust versions assume probability distributions and rank correlations for part of the data. Spreadsheets The data and the above-mentioned linkage are implemented in three spreadsheet files, including versions for deterministic optimization, stochastic optimization, and robust optimization: - MLFCP deterministic (V5).xlsx - MLFCP stochastic (V5).xlsx - MLFCP robust (V6).xlsx For a detailed description of the spreadsheet implementations and information on the software required to use them, see the associated data article published in Data in Brief. For the conceptual framework, mathematical formulation of the optimization model (MLFCP), findings, and discussion, see the associated research article published in Heliyon. (The links to both articles can be found on this page). Big Picture Furthermore, an overview (“big picture”) of the data flows between the various worksheets is provided in three versions which correspond to the deterministic, stochastic, and robust versions of the MLFCP: - Overview of data flows - deterministic (V5) - Overview of data flows - stochastic (V5) - Overview of data flows - robust (V6) Within each version of the overview, two file formats (PDF and PNG) are available. (Remark on version numbers: The version numbers reported in the file names and files might be lower than the version number of the entire dataset in case particular files remain unchanged in an update.)