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Record W110394087

A Closer Look at Rolling Budgets: The Challenges Associated with an Effective Implementation of Rolling Budgets Are Management Challenges, and Software Technology Can Only Become Part of the Solution When Managers Are Ready to Use It to Enhance Their Decision Making

2004· article· en· W110394087 on OpenAlexaboutno aff
Marc P. Lynn, Roland L. Madison

Bibliographic record

VenueManagement accounting quarterly · 2004
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCapital Investment and Risk Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsFlexibility (engineering)Plan (archaeology)Quarter (Canadian coin)Operations managementSoftwareBusinessOperations researchEconomicsFinanceMarketingEngineeringComputer scienceManagement
DOInot available

Abstract

fetched live from OpenAlex

Businesses are increasingly using rolling budgets. Also called continuous budgeting, rolling budgets always involve maintaining a plan for a specified time period in the future. To implement rolling budgets, many advocate leveraging new technological resources, which means software. It must be understood that the technology (e.g., bolt-on software packages) is not the solution. It is a tool by which and an environment in which management can have the opportunity to develop solution sets. Published surveys of financial officers of the largest industrial companies in the United States, Australia, Holland, Japan, and the United Kingdom show a number of interesting similarities as well as differences in budgeting practices across countries. (1) First, the use of master budgets is very widespread in all of these countries. Another significant finding is that financial managers in many countries distinguish between cost behavior patterns--variable versus fixed costs--for a common reason: They want to prepare more meaningful budgets by building flexibility into the model. How do these facts impact the concept of rolling budgets? Rolling budgets always involve maintaining a plan for a specified time period in the future. This result is achieved by adding a new time period in the future as the current time period that ended is dropped. Large companies, such as Electrolux and General Electric, prepare strategic plans and then integrate annual operating budgets that are divided into four-quarter rolling budgets, and smaller high-tech public companies, such as Keithley Instruments in Solon, Ohio, follow a similar pattern of planning. The annual operating budgets are prepared based upon best estimates of what management expects to occur and wants to achieve during the coming year. Flexibility is built into the process by considering how costs and revenues will change if different levels of activity occur (e.g., budgeting), and each quarter's changes are made to reflect changes in the economic and financial environment--things such as what the competition is doing, how the economy is spending for capital goods, and any planned changes in their product mix (adding or dropping a product line). In short, sound managers operate an entity with one eye always on the horizon, and a well-prepared business plan as reflected in a flexible rolling can be one of the financial managers' best tools to assist them in their role of planning and controlling the operations of this company. In his article Budgets on a Roll, Randy Myers identified a number of problems with annual static budgets. (2) A closer look, however, reveals that these problems were really management or human resource problems, where the proper development and use of budgets as just described was simply not understood. One example cited was that of an account director who would land several large clients early in the year and make his annual and then coast the rest of the year. This is not a problem with the budgeting process. It is a prime example of inept management and human resource functions that do not know how to plan and develop proper incentive systems. COSTLY SOFTWARE CANNOT HELP POOR MANAGEMENT The implementation of costly software based upon fixed algorithms that merely permit one to roll the budget forward on a monthly basis without looking at the big picture is not a solution for poor planning or for a lackluster management team. If the management of any company allows its sales force to play such games in the planning process, shareholders likely would not value the financial expenditure for software that merely accelerates the game. Maybe heads should roll before the budget rolls. Electronic spreadsheets such as Microsoft's Excel may be widely used for supporting the budgeting process, but if the data to populate the spreadsheets does not come from the corporate database directly, maintaining data integrity is a real problem. …

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.040
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.011
Science and technology studies0.0050.010
Scholarly communication0.0230.060
Open science0.0020.005
Research integrity0.0070.021
Insufficient payload (model declined to judge)0.0140.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.245
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2004
Admission routes1
Has abstractyes

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