John F. Affisco

Task and technology fit: a comparison of two technologies for synchronous and asynchronous group communication

With the increasing use of emerging communication technologies for collaborative work and group communication, organizations must recognize the benefits as well as the limitations of these technologies for communication effectiveness. An experiment was conducted to examine the interaction between task structure and technology to support synchronous and asynchronous group communication. Two communication technologies, e-mail and a group support system (GSS), and two levels of task structure (less structured and more structured) were used. Group outcomes were measured as total number of unique ideas generated, which were further decomposed into basic and inferential idea categories. Results indicate that GSS-supported groups generated more total and basic ideas. However, groups using e-mail performed a deeper problem analysis as indicated by a higher proportion of inferential ideas generated by these groups. The number and proportion of inferential ideas were also significantly higher in the less structured task than those in the more structured one.

E‐government: a strategic operations management framework for service delivery

Purpose – This paper aims to present a conceptual framework for selecting and developing e‐government applications as part of an overall coherent strategy of e‐government service delivery (EGSD).Design/methodology/approach – The framework proposed in this paper builds on and expands prior literature in the areas of e‐government and service delivery in organizations. Specifically, the framework extends Hesketts work in service delivery in organizations to the area of e‐government.Findings – The strategic operations management framework for EGSD developed in this paper represents a response to the need for a more strategic point of view on the electronic delivery of government services. The framework consists of seven building‐blocks – four basic elements: market segmentation, service mode development, operations‐strategy redesign, and service delivery, in addition to three integrative elements: differentiation, leverage of value and alignment of strategy and system.Research limitations/implications – The ...

Setup cost reduction in an inventory model with finite-range stochastic lead times

SUMMARY Traditionally, upon solution, independent demand inventory models result in the determination of a closed form for the economic lot size. Generally, this is obtained from the result that holding costs and setup costs are constant and equal at the optimum. However, the experience of the Japanese indicates that this need not be the case. Specifically, setup cost may be reduced by investing in reduced setup times resulting in smaller lot sizes and increased flexibility. Various authors have investigated the impact of such investment on classical lot sizing formulas which has resulted in the derivation of modified relationships. A common assumption of this research has been that demand and lead time are deterministic. This paper extends this previous work by considering the more realistic case of investing in decreasing setup costs where lead time is stochastic. Closed form relationships for optimal lot size, optimal setup cost, optimal total cost, etc. are derived. Numerical results are presented for...

Quality improvement and setup reduction in the joint economic lot size model

Abstract The co-maker concept has become accepted practice in many successful global business organizations. This fact has resulted in a class of inventory models known as joint economic lot size (JELS) models. Heretofore such models assumed perfect quality production on the part of the vendor. This paper relaxes this assumption and proposes a quality-adjusted JELS model. In addition, classical optimization methods are used to derive models for the cases of setup cost reduction, quality improvement, and simultaneous setup cost reduction and quality improvement for the quality-adjusted JELS. Numerical results are presented for each of these models. Comparisons are made to the basic quality-adjusted model. Results indicate that all three policies exhibit significantly reduced total cost. However, the simultaneous model results in the lowest cost overall and the smallest lot size. This suggests a synergistic impact of continuous improvement programs that focus on both setup and quality improvement of the vendors production process. Sensitivity analysis indicates that the simultaneous model is robust and representative of practice.

Environmental versus quality standards ‐ an overview and comparison

Considers the series of environmental standards known as ISO 14000. Presents an overview of the organization‐processes group of these standards, and a comparison with the existing quality standards ‐ ISO 9000 and the Malcolm Baldrige National Quality Award. ISO 14000 is concerned with establishing guidelines and principles for the management of environmental matters by organizations, through the establishment and operation of an environmental management system (EMS). Finds there is synergy between a quality management system (QMS) and an EMS; that like a QMS, an EMS must be an integral part of an organization’s overall management system; and that like a QMS, the design of an EMS is an ongoing process of continuous improvement. Concludes with several proposed research questions.

Lead-time variability reduction in stochastic inventory models

Abstract This research is motivated by the realization that Japanese manufacturers have devoted much time and effort to establish a long term partnership with their suppliers in order to reduce lead-time uncertainty. It is very common for a Japanese manufacturer to advance money to finance its suppliers and help them meet the rigid delivery standards imposed. However, there have been very few mathematical analyses of the advantages of investing in such efforts. The main objective of this paper is to provide an analytical model to quantify the tradeoffs associated with lower lead-time uncertainty. In particular, we consider the option of investing in order to reduce the lead-time variance in an inventory model with stochastic lead time. The paper also considers the option of investing in both setup cost and lead-time variability reduction. Numerical results are presented to demonstrate the use of the models. Sensitivity analysis is performed to indicate under what conditions investment is warranted.

A comparison of alternative joint vendor-purchaser lot-sizing models

Abstract This paper provides a comparative analysis of two sets of alternative joint lot-sizing models for the general one-vendor, many-nonidentical-purchasers case. Specifically, the basic joint economic lot size (irJELS) and individually responsible and rational decision (IRDD) models, and the simultaneous setup cost and order cost reduction versions are explored. Models for the latter situation are derived by the use of classical optimization techniques. A numerical example is presented which provides the basis for comparison of the models with the results of independent optimization (IO). For the basic models the previously reported advantages of IRRD are refuted. In the simultaneous investment case both the vendor and the purchasers realize significant savings over IO when the JELS policy is followed. This is not true for IRRD. This suggests that when an environment of co-operation between the parties has been established the JELS is a superior policy.

Quality improvement in an inventory model with finite-range stochastic lead times

Typically, traditional inventory models operate under the assumption of perfect quality. In this paper we modify an inventory model with finite-range stochastic lead time to allow for a random number of defective units in a lot. However, there is an extra cost for holding the defective items in the lot for the period before it is returned to the supplier. This paper also considers the option of investment to improve quality. Closed-form relationships are obtained for a quality-adjusted model as well as a quality improvement model. Numerical examples confirm that the option of investment in quality improvement results in significant cost savings. Sensitivity analysis shows that the quality improvement model is robust.

Investing in Lead-Time Variability Reduction in a Quality-Adjusted Inventory Model with Finite-Range Stochastic Lead-Time

We study the impact of the efforts aimed at reducing the lead-time variability in a quality-adjusted stochastic inventory model. We assume that each lot contains a random number of defective units. More specifically, a logarithmic investment function is used that allows investment to be made to reduce lead-time variability. Explicit results for the optimal values of decision variables as well as optimal value of the variance of lead-time are obtained. A series of numerical exercises is presented to demonstrate the use of the models developed in this paper. Initially the lead-time variance reduction model (LTVR) is compared to the quality-adjusted model (QA) for different values of initial lead-time over uniformly distributed lead-time intervals from one to seven weeks. In all cases where investment is warranted, investment in lead-time reduction results in reduced lot sizes, variances, and total inventory costs. Further, both the reduction in lot-size and lead-time variance increase as the lead-time interval increases. Similar results are obtained when lead-time follows a truncated normal distribution. The impact of proportion of defective items was also examined for the uniform case resulting in the finding that the total inventory related costs of investing in lead-time variance reduction decrease significantly as the proportion defective decreases. Finally, the results of sensitivity analysis relating to proportion defective, interest rate, and setup cost show the lead-time variance reduction model to be quite robust and representative of practice.

An empirical investigation of integrated multicriteria group decision models in a simulation/gaming context

This article proposes two models of group decision making that integrate mathematical and behavioral concepts. Further, the results of an empirical test of these two integrated spatial proximity multicriteria decision-making-problem-solving technology models are presented. The performance of these models is compared to the performance of a nonintegrated multicriteria model for two strategic operations decisions: plant location and process selection. The empirical test utilized free simulation methodology and was conducted in the context of the Business Management Laboratory simulation game. Results include the finding that both the integrated and nonintegrated models generally described choices more accurately than a random process did. Findings relating the performance of the integrated models to that of the nonintegrated model were mixed. For the process selection decision, one integrated model, TOPSIS-PST, outperformed the others. Additional results indicate that the number of decision criteria might be a critical factor to consider when selecting a multicriteria decision-making model Finally, the study showed free simulation to be a valuable methodology for the study of multicriteria decision making.