Marc J. Schniederjans

Using the Analytic Hierarchy Process and multi-objective programming for the selection of cost drivers in activity-based costing

Abstract Activity-Based Costing (ABC) makes use of multiple cost drivers. The selection of these cost drivers from a set of candidate cost drivers can pose a difficult selection problem. This paper proposes the use of the Analytic Hierarchy Process (AHP) and a multi-objective programming methodology as aids in making cost driver selections. A demonstration of the application of these methodologies in ABC cost driver selection is presented. The informational efficacy of the proposed combined methodologies is also discussed.

A comparison between Fama and French's model and artificial neural networks in predicting the Chinese stock market

Evidence exists that emerging market stock returns are influenced by a different set of factors than those that influence the returns for stocks traded in developed countries. This study uses artificial neural networks to predict stock price movement (i.e., price returns) for firms traded on the Shanghai stock exchange. We compare the predictive power using linear models from financial forecasting literature to the predictive power of the univariate and multivariate neural network models. Our results show that neural networks outperform the linear models compared. These results are statistically significant across our sample firms, and indicate neural networks are a useful tool for stock price prediction in emerging markets, like China.

Implementing enterprise resource planning systems with total quality control and business process reengineering

The primary objective of an enterprise resource planning (ERP) system is to help integrate an organizations business operations and processes effectively and efficiently. Not all firms have been successful in their ERP implementations and to that end research has helped to identify many factors that might be critical to a successful implementation. Such factors as the use of business process reengineering (BPR), and establishing a total quality management (TQM) culture have all shown to play important roles in ERP implementation. The focus of this survey research on US electronic manufacturing firms is to identify successful integration sequences of TQM and BPR with ERP. The findings reveal that both the sequence of implementation and the strategies selected to initiate ERP systems can significantly impact business performance successfulness.

U-shaped assembly line layouts and their impact on labor productivity: An experimental study

Abstract The decision to move straight-line assembly systems to U-shaped assembly lines systems constitutes a major layout design change and investment for assembly operations. Proponents of the lean manufacturing and just-in-time philosophies assert that U-shaped assembly systems offer several benefits over traditional straight-line layouts including an improvement in labor productivity. This premise often serves as the fundamental reason why firms consider transforming their assembly systems from traditional straight-lines to U-shaped layouts. Surprisingly, little empirical or experimental data supports this assertion. The purpose of this research is to empirically confirm that U-shaped assembly lines improve labor productivity. Results indicate that labor productivity will improve significantly under certain conditions when switching from a straight-line layout to a U-shaped layout but not in all cases. The research also reveals some limitations of such a layout change when factors such as the number of tasks and cycle times are varied.

Information Technology Investment: Decision-Making Methodology

From the individual to the largest organization, everyone today has to make investments in IT. Making a smart investment that will best satisfy all the necessary decision-making criteria requires careful and inclusive analysis. This textbook provides an up-to-date, in-depth understanding of the methodologies available to aid in this complex process of multi-criteria decision-making. It guides readers on the process of technology acquisition — what methods to use to make IT investment decisions, how to choose the technology and justify its selection, and how the decision will impact the organization.

Neural Network Earnings per Share Forecasting Models: A Comparative Analysis of Alternative Methods

In this paper, we present a comparative analysis of the forecasting accuracy of univariate and multivariate linear models that incorporate fundamental accounting variables (i.e., inventory, accounts receivable, and so on) with the forecast accuracy of neural network models. Unique to this study is the focus of our comparison on the multivariate models to examine whether the neural network models incorporating the fundamental accounting variables can generate more accurate forecasts of future earnings than the models assuming a linear combination of these same variables. We investigate four types of models: univariate-linear, multivariate-linear, univariate-neural network, and multivariate-neural network using a sample of 283 firms spanning 41 industries. This study shows that the application of the neural network approach incorporating fundamental accounting variables results in forecasts that are more accurate than linear forecasting models. The results also reveal limitations of the forecasting capacity of investors in the security market when compared to neural network models.

A goal programming model to optimize departmental preference in course assignments

Abstract This paper describes an application of a zero-one goal programming approach at the University of Nebraska to allocate teaching staff to specific courses based on departmental needs and the personal preferences of departmental faculty. The results of the application demonstrate the models capability to provide an assignment that satisfies departmental course offerings and teaching load objectives, while at the same time recognizing the personal preferences of the faculty concerned in the assignment process.

Using Goal Programming and the Analytic Hierarchy Process in House Selection

The house selection process involves an assessment by the buyer of a series of qualitative and quantitative factors. Simple ranking or weighting selection methods utilizing these factors can lead to less than optimal decision making. This paper presents a model to aid in the house selection decision process. Specifically, this paper presents a Goal Programming (GP) model that utilizes the Analytic Hierarchy Process (AHP) to evaluate property attributes and make an optimal house selection decision. The formulation methodology for the proposed house selection modeling process is illustrated with examples based on data from a prior study.

An alternative analysis of inventory costs of JIT and EOQ purchasing

Recent models comparing inventory costs under just‐in‐time (JIT) purchasing plans and economic order quantity (EOQ) purchasing plans have tended to favor EOQ purchasing in situations where annual demand of inventory is moderately large. Contends that these cost models are lacking dynamic cost components inherent in virtually all JIT purchasing plans. Presents a series of inventory purchasing cost models that extend prior methodology by Fazel by including relevant physical distribution cost savings. Additional comparative models are presented to further demonstrate how other relevant costs factors can be included in a comparative EOQ/JIT model. A cost comparison with an existing problem from the literature is used to illustrate the informational efficacy of new models.

Empirical study of the relationship between operations strategy and information systems strategic orientation in an e-commerce environment

Many corporate executives are convinced that todays e-commerce operations require a fundamental review of business strategy. Operations management researchers also call for substantiating operations strategy research to an e-commerce environment. However, no empirical research has explored information systems strategic issues using operations strategy theory. Drawing on both operations strategy and the information systems strategy literature, this exploratory research proposes a conceptual framework integrating both operations strategy and information systems strategy models, and then applies this new proposed framework to an e-commerce setting. This research not only provides a conceptual framework systematically to explore e-commerce strategic issues, but also provides empirical evidence on the relationships between its various constructs. Results of this exploratory study indicate that the business environment appears to have a tangible impact on strategic choices in operations. It also appears that links between the business environment and operations strategy in e-commerce, as well as the alignment between the information systems strategic orientation and operations strategy, determines e-commerce business performance success. Results show that high-performing e-commerce companies and low-performing e-commerce companies use different information systems strategies to support their operations strategy. The types of strategies these companies use are identified in this study.