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Data, data flows, and model specifications for linking multi-level contribution margin accounting with multi-level fixed-charge problems.

ABSTRACT: This article describes the data, data flows, 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. This linkage can reveal previously hidden optimization potentials within the framework of multi-level contribution margin accounting, thus providing better information for decision making in companies and other organizations. 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 needed to analyze the profitability of a company´s products and organizational entities in the presence of hierarchically structured fixed costs. The data are processed and analyzed by means of mathematical optimization techniques and sensitivity analysis. The linkage between multi-level contribution margin accounting and MLFCP is implemented in three spreadsheet files, including versions for deterministic optimization, stochastic optimization, and robust optimization. This paper provides specifications for compatible solver add-ins and for executing sensitivity analysis. The data and spreadsheet implementations described in this article were used in a research article entitled "Making better decisions by applying mathematical optimization to cost accounting: An advanced approach to multi-level contribution margin accounting" [1]. The data sets and the spreadsheet implementations may be reused a) by researchers in management and cost accounting as well as in operations research and quantitative methods for verification and for further development of the linkage concept and of the underlying optimization models; b) by practitioners for gaining insight into the data requirements, methods, and benefits of the proposed linkage, thus supporting continuing education; and c) by instructors in academia who may find the data and spreadsheets valuable for classroom use in advanced courses. The complete spreadsheet implementations in the form of three ready-to-use Excel files (deterministic, stochastic, and robust version) are available for download at Mendeley Data. They may serve as customizable templates for various use cases in research, practice, and education.

SUBMITTER: Gutierrez M

PROVIDER: S-EPMC8010628 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The purpose of multi-level contribution margin accounting in cost accounting is to analyze the profitability of products and organizational entities with appropriate allocation of fixed costs and to provide relevant information for short-term, medium- and longer-term decisions. However, the conventional framework of multi-level contribution margin accounting does not usually incorporate a mathematical optimization method that simultaneously integrates variable and fixed costs to determine the best possible product mix within hierarchically structured organizations. This may be surprising in that operations research provides an optimization model in the form of the fixed-charge problem (FCP) that takes into account not only variable costs but also fixed costs of the activities to be planned. This paper links the two approaches by expanding the FCP to a multi-level fixed-charge problem (MLFCP), which maps the hierarchical decomposition of fixed costs in accordance with multi-level contribution margin accounting. In this way, previously hidden optimization potentials can be made visible within the framework of multi-level contribution margin accounting. Applying the linkage to a case study illustrates that the original assessment of profitability gained on the sole basis of a multi-level contribution margin calculation might turn out to be inappropriate or even inverted as soon as mathematical optimization is utilized: products, divisions, and other reference objects for fixed cost allocation, which at first glance seem to be profitable (or unprofitable) might be revealed as actually unprofitable (or profitable), when the multi-level contribution margin calculation is linked to the MLFCP. Furthermore, the proposed concept facilitates assessment of the costs of an increasing variant diversity, which also demonstrates that common rules on how to interpret a multi-level contribution margin calculation may have to be revised in some cases from the viewpoint of optimization. Finally, the impact of changes in the fixed cost structure and other parameters is tested via sensitivity analyses and stochastic optimization.

Project description:Variation in the delivery of evidence-based care affects outcomes for patients with stroke. A range of hospital (organizational), patient, and clinical factors can affect care delivery. Clinical registries are widely used to monitor stroke care and guide quality improvement efforts within hospitals. However, hospital features are rarely collected. We aimed to explore the influence of hospital resources for stroke, in metropolitan and regional/rural hospitals, on the provision of evidence-based patient care and outcomes. The 2017 National Audit organizational survey (Australia) was linked to patient-level data from the Australian Stroke Clinical Registry (2016-2017 admissions). Regression models were used to assess the associations between hospital resources (based on the 2015 Australian National Acute Stroke Services Framework) and patient care (reflective of national guideline recommendations), as well as 90-180-day readmissions and health-related quality of life. Models were adjusted for patient factors, including the severity of stroke. Fifty-two out of 127 hospitals with organizational survey data were merged with 22 832 Australian Stroke Clinical Registry patients with an admission for a first-ever stroke or transient ischaemic attack (median age 75 years, 55% male, and 66% ischaemic). In metropolitan hospitals (n = 42, 20 977 patients, 1701 thrombolyzed, and 2395 readmitted between 90 and 180 days post stroke), a faster median door-to-needle time for thrombolysis was associated with ≥500 annual stroke admissions [-15.9 minutes, 95% confidence interval (CI) -27.2, -4.7], annual thrombolysis >20 patients (-20.2 minutes, 95% CI -32.0, -8.3), and having specialist stroke staff (dedicated medical lead and stroke coordinator; -12.7 minutes, 95% CI -25.0, -0.4). A reduced likelihood of all-cause readmissions between 90 and 180 days was evident in metropolitan hospitals using care pathways for stroke management (odds ratio 0.82, 95% CI 0.67-0.99). In regional/rural hospitals (n = 10, 1855 patients), being discharged with a care plan was also associated with the use of stroke clinical pathways (odds ratio 3.58, 95% CI 1.45-8.82). No specific hospital resources influenced 90-180-day health-related quality of life. Relevant to all international registries, integrating information about hospital resources with clinical registry data provides greater insights into factors that influence evidence-based care.

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Data, data flows, and model specifications for linking multi-level contribution margin accounting with multi-level fixed-charge problems.

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