A Simulation Analysis for Ranking and Selecting the Best Combination of Production Planning and Accounting Control Systems.

Abstract

Frequently, the objective of computer simulation experiments of accounting and business systems is to find the best policy, procedure, or decision rule. Previous accounting or business simulation studies assumed that observations taken from each population were normally distributed with unknown means and known or equal variances. Unfortunately, only in rare cases can such assumptions be expected to hold. This paper introduces a multiple-ranking procedures, which allows for unknown and unequal variances, to analyze simulations of production planning and accounting control systems. The model under study is a hypothetical firm with profit and sales as multiple objectives. Two multiple objective planning models with uncertain demands are formulated, and two accounting variance analysis techniques are used and incorporated into the two planning models. A two-stage sampling procedure is used to determine the sample size. The simulated data are analyzed by a multiple-ranking procedure and then the best policy with respect to profit and sales is selected separately. Possible accounting and business applications are also mentioned. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1982
Accession Number
ADA113427

Entities

People

  • Edward J. Dudewicz
  • Hubert J. Chen
  • W. Thomas Lin

Organizations

  • Ohio State University

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Analysis Of Variance
  • Computational Science
  • Computer Science
  • Computer Simulations
  • Control Systems
  • Data Science
  • Goal Programming
  • Information Science
  • Inventory
  • Knowledge Management
  • Linear Programming
  • Probability
  • Production Planning
  • Simulations
  • Statistical Processes
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Government Contracting/Procurement.
  • Logistics and Supply Chain Management.
  • Regression Analysis.