ACCOUNTING SYSTEMS: THE RELIABILITY APPROACH TO INTERNAL CONTROL EVALUATION

Recent developments have increased the value to management of objective methodologies for the evaluation of accounting internal control systems (AICS). The Foreign Corrupt Practices Act requires management to assure the accuracy of these systems. The process of evaluation of AICS by both management and external auditors is judgmental in nature. The few attempts at modeling AICS have not been implemented by firms due to the lack of realism, difficulty of modeling behavioral systems, lack of cost effectiveness, and lack of understanding by practitioners. For these reasons, the role of the decision scientist has been limited. The research reported here attempts to demonstrate feasibility of a reliability model. A typical accounting system for raw material purchases is modeled via a simulation routine. The effects of internal control weaknesses on ending dollar balances is determined by performing sensitivity analysis. After summarizing these effects, the reliability model is formulated for the same control system, and the analysis is repeated and the results compared with those obtained in a simulation model.     

一个单件车间生产作业计划优化模拟系统

针对中小批量单件车间生产作业计划生成与再生问题,集成Visual Foxpro5.0与Siman 3.51开发了一个系统原型,包括模拟模型生成器、时间参数推算器和禁忌搜索算法。用模拟模型生成器得到一个比较详细的中小批量单件车间生产作业计划方案,以此为初始可行解,再用时间参数推算器和禁忌搜索算法进行优化,得到一个优化了的车间生产作业计划方案。实验表明,本系统较好地解决了中小批量单件车间生产作业计划生成问题。     

Multi-objective simulation optimization using data envelopment analysis and genetic algorithm: Specific application to determining optimal resource levels in surgical services

Simulation is a powerful tool for modeling complex systems with intricate relationships between various entities and resources. Simulation optimization refers to methods that search the design space (i.e., the set of all feasible system configurations) to find a system configuration (also called a design point) that gives the best performance. Since simulation is often time consuming, sampling as few design points from the design space as possible is desired. However, in the case of multiple objectives, traditional simulation optimization methods are ineffective to uncover the efficient frontier. We propose a framework for multi-objective simulation optimization that combines the power of genetic algorithm (GA), which can effectively search very large design spaces, with data envelopment analysis (DEA) used to evaluate the simulation results and guide the search process. In our framework, we use a design point's relative efficiency score from DEA as its fitness value in the selection operation of GA. We apply our algorithm to determine optimal resource levels in surgical services. Our numerical experiments show that our algorithm effectively furthers the frontier and identifies efficient design points.     

Order Dispatching and Labor Assignment in Cellular Manufacturing Systems

Although order and labor dispatching in the job shop manufacturing setting have been investigated extensively over the last three decades, its representation of actual processes found in practice today is limited due to the move to cellular manufacturing (CM). Manufacturing cells have become an important approach to batch manufacturing in the last two decades, and their layout structure provides a dominant flow structure for the part routings. The flow shop nature of manufacturing cells adds a simplifying structure to the problem of planning worker assignments and order releases, which makes it more amenable to the use of optimization techniques. In this paper we exploit this characteristic and present two mathematical modeling approaches for making order dispatching and labor assignment/reassignment decisions in two different CM settings. The two formulations are evaluated in a dynamic simulation setting and compared to a heuristic procedure using tardiness as the primary performance measure. The formulations are superior to the heuristic approach and can be incorporated into detail scheduling systems that are being implemented by corporations employing enterprise resource planning (ERP) systems today.     

Distributed manufacturing support systems: the integration of distributed group support systems with manufacturing support systems

Current Manufacturing Support Systems (MSS), such as Computer-aided Manufacturing (CAM) Systems, Computer-aided Design (CAD) Systems, Computer Integrated Manufacturing (CIM) Systems, Material Resource Planning (MRP) Systems, and Manufacturing Accounting Control (MAC) Systems, are mostly independent systems that are operated in limited decision spaces, provide mainly formal and quantitative information, and thus pursue a goal of local optimization. To assist modern manufacturing in meeting the needs for integration, communication, collaboration, and decision making, we introduce the concept of integrating MSS with Distributed Group Support System (DGSS) into a Distributed Manufacturing Support System (DMSS). A rigorous system design approach is taken to model the manufacturing information requirements from a global perspective and pattern decision making processes within the structural (organizational design) and infrastructural (information system design) elements of manufacturing. The result is a conceptual DMSS design that provides an intelligent interface, accommodates incremental manufacturing integration, offers controllable message exchange facilities, and allows configurable communication networks.     

Functional and Behavioral Application

Simulation techniques have been recommended in the recent literature as vehicles for improving the analysis of the corporate capital budgeting decision. Such techniques are alleged to provide more helpful measures of both return and risk than do single-point discounted cash flow estimates of project worth. This contention is challenged here. The conclusion is reached that the information provided by simulation is, at best, no better than is generated by the traditional single-point present value approach and, in one very important respect, is markedly inferior.     

Simulation optimization through direct search for multi-objective manufacturing systems

Simulation modelling has been one of the most widely used techniques for analysing complex manufacturing systems. In this paper, we propose a direct search algorithm expanded from the Hooke-Jecves pattern search to systematically and efficiently locate satisfactory solutions for multi-objective simulation models. The user-specified goals can be precise and/or fuzzy. Heuristic rules stemming from the simulation result of resource statistics are incorporated into the Hooke-Jeeves pattern search. The proposed heuristic rules make the search procedure effective regardless of different initial points and various bounded ranges of decision variables. Experimental results show that the proposed approach is suitable for analysing complex manufacturing systems, in which multiple objectives and multiple decision variables are encountered.     

METHODOLOGICAL ANALYSIS THROUGH SYSTEMS SIMULATION

Simulation techniques are widely used for analyzing the policy decisions of managers. This paper presents a “higher-level” use of simulation to analyze the modeling decisions of analysts, which models are, in turn, useful for policy analysis. Methodological systems simulation (MSS) is described in terms of a systems performance prediction problem. The MSS program is outlined and the two major uses of MSS-evaluation of alternative models and simulation of the analyst–are described in terms of the results of simulation runs using the systems performance prediction context.     

Sensitivity of Physiologically Based Pharmacokinetic Models to Variation in Model Parameters: Methylene Chloride

The parameters in a physiologically based pharmacokinetic (PBPK) model of methylene chloride were varied systematically, and the resulting variation in a number of model outputs was determined as a function of time for mice and humans at several exposure concentrations. The importance of the various parameters in the model was highly dependent on the conditions (concentration, species) for which the simulation was performed and the model output (dose surrogate) being considered. Model structure also had a significant impact on the results. For sensitivity analysis, particular attention must be paid to conservation equations to ensure that the variational calculations do not alter mass balance, introducing extraneous effects into the model. All of the normalized sensitivity coefficients calculated in this study ranged between −1.12 and 1, and most were much less than 1 in absolute value, indicating that individual input errors are not greatly amplified in the outputs. In addition to ranking parameters in terms of their impact on model predictions, time-dependent sensitivity analysis can also be used as an aid in the design of experiments to estimate parameters by predicting the experimental conditions and sampling points which will maximize parameter identifiability.     

A systems simulation model of the Kenyan economy

This paper describes a systems simulation model of the national economy of Kenya. The model contains an input/output production component linked to a consumption component, disaggregated into nine income classes. Capital formation and government are integrated into the model as interactive elements. The model is demand driven and thus growth rates in the productive sectors are generated endogenously as a function of demand. The model has been used by the Kenyan Ministry of Finance and Economic Planning for forecasting and policy evaluation problems. A contributory factor in the successful implementation of the model is its ability to supply detailed quantitative forecasts which, in a developing country, are not readily available from routine sources. In addition, the model deals explicitly with income distribution and inflation consequences which are issues of current concern to local development planners.     

Review and preview of productivity research and applications

During the last two decades, productivity research and applications have not been given adequate importance when trying to attain excellence in the management of manufacturing enterprises. Recent developments in managerial philosophies Total Quality Management and Business Process Re-engineering , manufacturing technologies Flexible Manufacturing Systems, Computer Integrated Manufacturing, etc. and Information Technology innovations have made the traditional productivity improvement techniques obsolete. This article presents a review consisting of analyses of literature on productivity and a survey of manufacturing enterprises. A five-step preview strategy on productivity is enumerated which provides a meaningful direction towards future productivity research and application. The article is concluded by briefly describing the current research that is being carried out based on the preview strategy evolved.     

Earliness/Tardiness Production Scheduling and Planning,and solutions

Earliness/Tardiness Production Planning (ETPSP) has attracted much attention in recent years. In practice, it provides an efficient way to integrate Manufacturing Resource Planning (MRP-II) Scheduling and with Just-In Time (JIT). Conventional research mainly focused on the condition of single-machine and parallel multi-machine. Capacity balance has not been considered. Some investigations of existing research on ETPSP are given in this paper. An extensive model of ETPSP with consideration of multi-process capacity balance for manufacturing systems is proposed. Two alternative solutions, key-process method and relaxation method, are both developed to solve ETPSP. Computational results identify that both methods are powerful to solve ETPSP which minimizes total earliness and tardiness penalties. Furthermore, some comparisons between these two methods and other conventional methods indicate the efficiency of the former.     

Joint production and subcontracting planning of unreliable multi-facility multi-product production systems

This article addresses the problem of joint optimization of production and subcontracting of unreliable production systems. The production system considered presents a common problem in the pharmaceutical industry. It is composed of multiple production facilities with different capacities, each of which is capable of producing two different classes of medications (brand name and generic). The resort to subcontracting is double: first, it involves the quantity of products received on a regular basis in order to compensate for insufficient production capacity in existing facilities, second, when needed, urgent orders are also launched in order to reduce the risk of shortages caused by breakdowns of manufacturing facilities. Failures, repairs and urgent delivery times may be represented by any probability distributions.The objective is to propose a general control policy for the system under consideration, and to obtain, in the case of two facilities, optimal control parameters that minimize the total incurred cost for a specific level of the customer service provided. Given the complexity of the problem considered, an experimental optimization approach is chosen in order to determine the optimal control parameters. This approach includes experimental design, analysis of variance, response surface methodology and simulation modeling. It allows the accurate representation of the dynamic and stochastic behaviors of the production system and the assessment of optimal control parameters. Other control parameters which represent the subcontracting are introduced and three joint production/subcontracting control policies (general, urgent, regular) are compared to one another. The proposed joint production/regular subcontracting control policy involves a cost decrease of up to 20%, as compared to results obtained by Dror et al. [1], who used a simplified control policy in addition to a heuristic solution approach for a real case study. This policy offers not only cost savings, but is also easier to manage, as compared to that proposed by Dror et al. [1]. Numerical examples and a sensitivity analysis are also performed to illustrate the robustness of the proposed control policy and the solution approach.     

Sensitivity Analysis Using Risk Measures

In a quantitative model with uncertain inputs, the uncertainty of the output can be summarized by a risk measure. We propose a sensitivity analysis method based on derivatives of the output risk measure, in the direction of model inputs. This produces a global sensitivity measure, explicitly linking sensitivity and uncertainty analyses. We focus on the case of distortion risk measures, defined as weighted averages of output percentiles, and prove a representation of the sensitivity measure that can be evaluated on a Monte Carlo sample, as a weighted average of gradients over the input space. When the analytical model is unknown or hard to work with, nonparametric techniques are used for gradient estimation. This process is demonstrated through the example of a nonlinear insurance loss model. Furthermore, the proposed framework is extended in order to measure sensitivity to constant model parameters, uncertain statistical parameters, and random factors driving dependence between model inputs.     

STATE-OF-THE-ART SURVEY: OPEN QUEUEING NETWORKS: OPTIMIZATION AND PERFORMANCE EVALUATION MODELS FOR DISCRETE MANUFACTURING SYSTEMS

In this survey we review methods to analyze open queueing network models for discrete manufacturing systems. We focus on design and planning models for job shops. The survey is divided in two parts: in the first we review exact and approximate decomposition methods for performance evaluation models for single and multiple product class networks. The second part reviews optimization models of three categories of problems: the first minimizes capital investment subject to attaining a performance measure (WIP or lead time), the second seeks to optimize the performance measure subject to resource constraints, and the third explores recent research developments in complexity reduction through shop redesign and products partitioning.     

Some further results on minimum distribution cost flow problems

Manufacturing network flow (MNF) is a generalized network model that can model more complicated manufacturing scenarios, such as the synthesis of different materials to one product and/or the distilling of one material to many different products. Minimum distribution cost flow problem (MDCF) is a simplified version of MNF optimization problems, in which a general supplier wants to proportionally distribute certain amount of a particular product from a source node to several retailers at different destinations through a distribution network. A network simplex algorithm has been outlined in recent years for solving a special case of MDCF. In this paper, we characterize the network structure of the bases of the MDCF problem and develop a primal simplex algorithm that exploits the network structure of the problem. These results are extensions of those of the ordinary network flow problems. In conclusion, some related interesting problems are proposed for future research. This research is partially supported by the National Natural Science Foundation of China (No. 10371028) and a grant from Southern Yangtze University (No. 0003182).     

Critical evaluation of simulation studies in maintenance systems.

The purpose of this paper is to examine how far simulation studies of maintenance systems are neglecting simulation-related statistical issues. This negligence may leave simulation results suspicious and hard to explain. Several simulation factors are used to identify the strength of executed simulation experiments and to evaluate the level of clarity and reliability of the simulation models. The factors includes purpose of simulation, simulation model, model assumptions, distribution and random variables, simulation languages and computers, program verification and model validation, design of experiment, and analysis of the output. For this purpose, the literature is reviewed and subjected to the evaluation. It is observed that most papers define clearly their objectives, simulation languages, and model performance measures. However, verification, validation, experimental design, and output analysis are the most unclear factors.     

On‐Call Overtime for Service Workforce Scheduling when Demand Is Uncertain

This article builds on prior research to develop shift scheduling models that include on‐call overtime for service environments where demand is uncertain. The research is motivated by recent developments in nurse scheduling, such as laws prohibiting mandatory overtime and the popularity of self‐scheduling systems. For single‐period scenarios, models are developed, solution methods are described, and results are explored for a variety of environments. Results show that the use of on‐call overtime can reduce costs slightly, with the amount of savings dependent on characteristics of the scheduling environment. The factor that most significantly affects cost savings is the cost of outside agency workers relative to overtime workers. In addition to lowering costs, on‐call overtime greatly reduces reliance on outside agency workers, which can have important practical implications in terms of quality of service and workforce morale. Results based on single‐period models motivate multiperiod formulations for single‐ and multidepartment scenarios, and solution methods are outlined for those cases. The possibility of using multiperiod models within a rolling horizon framework with forecast updating is discussed. This goes along with an extension of the traditional workforce management hierarchy that separates overtime and regular‐time scheduling, as seen in practice with self‐scheduling and shift‐bidding systems.     

Exploring Risk Judgments in a Trade Dispute Using Bayesian Networks

Bayesian networks (BNs) are graphical modeling tools that are generally recommended for exploring what‐if scenarios, visualizing systems and problems, and for communication between stakeholders during decision making. In this article, we investigate their potential for exploring different perspectives in trade disputes. To do so, we draw on a specific case study that was arbitrated by the World Trade Organization (WTO): the Australia‐New Zealand apples dispute. The dispute centered on disagreement about judgments contained within Australia's 2006 import risk analysis (IRA). We built a range of BNs of increasing complexity that modeled various approaches to undertaking IRAs, from the basic qualitative and semi‐quantitative risk analyses routinely performed in government agencies, to the more complex quantitative simulation undertaken by Australia in the apples dispute. We found the BNs useful for exploring disagreements under uncertainty because they are probabilistic and transparently represent steps in the analysis. Different scenarios and evidence can easily be entered. Specifically, we explore the sensitivity of the risk output to different judgments (particularly volume of trade). Thus, we explore how BNs could usefully aid WTO dispute settlement. We conclude that BNs are preferable to basic qualitative and semi‐quantitative risk analyses because they offer an accessible interface and are mathematically sound. However, most current BN modeling tools are limited compared with complex simulations, as was used in the 2006 apples IRA. Although complex simulations may be more accurate, they are a black box for stakeholders. BNs have the potential to be a transparent aid to complex decision making, but they are currently computationally limited. Recent technological software developments are promising.