Margaret F. Shipley

FACTORS CONTRIBUTORY TO SUCCESS: A STUDY OF PAKISTAN'S SMALL BUSINESS OWNERS

Small business and entrepreneurship have been at the heart of Pakistans economy for almost 60 years, yet little (if any) research has been conducted that identifies factors crucial for small business success in Pakistan. In the past, studies identifying factors crucial for small business success have focused primarily on the United States and Western Europe. This paper presents survey results from 265 small business owners located in and around Karachi, the largest city and hub of economic activity in Pakistan. The survey was designed to identify the internal and external factors that Pakistani small businesspersons believe are critical for success.

Utilizing fuzzy compatibility of skill sets for team selection in multi-phase projects

Abstract In this paper, a model is developed for the selection of personnel for a multiple phase project which takes into account the match between the skills possessed by each individual, the skills needed for each phase, and rather flexible budget considerations. The algorithm uses the fuzzy construct of compatibility to measure the fit of a person’s skill set to the goal set for each project phase. Based on the individual fuzzy compatibility measures, the team is formed from combined levels of compatibility and acceptable levels of quality defined for the goal set. 1 Introduction , 2 Background present the background information necessary to an understanding of project management phases and compatibility of skills. The development of the model and subsequent algorithm in 3 Model development , 4 Application , respectively rely on fuzzy measures of compatibility. Finally, an application is presented in Section 4 with conclusions stated in Section 5 .

A decision making model for multi-attribute problems incorporating uncertainty and bias measures

Abstract Although a particular attribute, in isolation, may be considered as very important, it will not be of much use in differentiating among alternate decisions if all values for that particular attribute are very close. Another problem is in specifying weights indicating the attributes the decision maker considers important. These weights are difficult to obtain because they often reflect an attitude brought about by considering all of the alternatives rather than reflecting the decision makers behavior. This article addresses the preceding problems through successive iterations of a process by which the decision maker is presented with sets of alternatives that most closely satisfy his ideal choice. The process incorporates the uncertainty of the decision maker with respect to how much each alternatives attribute value satisfies his ideal value. Entropy is also used as a measure of uncertainty in obtaining the alternative that is as close as possible to the ideal. The bias of the decision maker is reflected in how much he feels each attribute will make him happy. Fuzzy set theory is used to cope with the vagueness of the decision makers feelings with respect to his satisfaction with an alternative and the relative importance of each attribute.

BIFPET methodology versus PERT in project management: fuzzy probability instead of the beta distribution

Abstract Research throughout the years has centered on the use of the beta distribution to model variable activity times in the Program Evaluation and Review Technique (PERT). Justification for using a weighted average of optimistic, most likely and pessimistic times is based mostly on the beta distributions ability to handle skewness and its ease of use for computing the mean activity times. This paper presents two variations of a fuzzy probability based model for project management. The Belief in Fuzzy Probability Estimations of Time (BIFPET) model uses human judgment instead of stochastic assumptions to determine project completion titties. Following a literature review of PERT critiques, background information is provided for BIFPET. Next, a foam block production machine project is described and solved based on three estimates of time for each activity. A variation of BIFPET that uses ranges on these time estimates is presented and the case is solved for fuzzy expected completion times. The results are compared to those derived by using PERT and benefits of the BIFPET approach are detailed. The paper concludes with a description of our ongoing research initiative in the area of fuzzy probability applications to project management.

A fuzzy approach for selecting project membership to achieve cognitive style goals

Decision makers select employees for a project to match a particular set of goals pertaining to the multiple criteria mix of skills and competencies needed. Cognitive style influences how a person gathers and evaluates information and consequently, provides skills and competencies toward problem solving. The proposed fuzzy set-based model facilitates the managers selection of employees who meet the project goal(s) for the preferred cognitive style. The paper presents background information on cognitive styles and fuzzy logic with an algorithm developed based on belief in the fuzzy probability of a cognitive style fitting a defined goal. An application is presented with analysis and conclusions stated.

A fuzzy logic-based decision model to satisfy goals for successful product/service introduction

Abstract The Dempster–Shafer theory of evidence is applied to a multiattribute decision-making problem where the decision maker must determine which of several products/services have the best opportunity for success in a competitive marketplace. Multiattribute decisions are generally constrained by the uncertainty inherent in assessing the relative importance of each attribute element that is needed for success and the evaluation of the product/service to be introduced. The relative importance of each attribute element deemed necessary for success is assessed by the decision maker as a goal to be met. The evaluation of each product/service is addressed through expert opinion about the degree to which each element is contained in each product/service. Then the belief and plausibility that a product/service will satisfy the decision makers goal are calculated. The decision to introduce a product or service depends on the evaluation of the anticipated loss from introduction of a product/service into a competitive market.

Assessing risks due to threats to internal control in a computer-based accounting information system: a pragmatic approach based on fuzzy set theory

A rational risk assessment model, based on the reasoning of fuzzy set theory, is presented. The model would help managers assess risk exposure due to potential threats to internal control in a computer-based accounting information system. Such risk assessment is essential in making appropriate decisions about establishing new internal control policies and procedures that may be necessary to protect the integrity and security of the information system. Copyright

Rough set theory fuzzy belief functions related to statistical confidence:application & evaluation for golf course closing

Rough set theory is developed in terms of fuzzy sets that will allow a decision maker to determine certain and possible rules. In the application presented these rules are determined for the decision to close a golf course when the number of rounds of golf to be played is anticipated to be low. Attributes having an effect on the number of rounds of golf to be played are statistically analyzed and those negative factors are selected for generating rules for closing. Furthermore, the degree of belief of the decision maker in these generated rules is determined to be directly related to statistical confidence intervals.

Sample size: achieving quality and reducing financial loss

Determining the proper sample size such that quality is assured while financial losses are not unnecessarily incurred is critical to an effective quality program. The main purpose of the present work is to design a fuzzy controller to adjust sample sizes according to potential fuzzy loss penalties. A set of fuzzy rules is given where, depending on the antecedents, the sample size may be decreased, moderately modified, or increased. At any given moment the proportion of defects in the sample determines the firing strength of the rules suggesting an appropriate sample size. These rules are then modified to include an analysis of the decision maker’s belief that in a particular situation an inappropriate rule is being considered such that an expected loss would be incurred in meeting or falling short of defined quality goals.

A fuzzy logic model for competitive assessment of airline service quality

The purpose of this paper is to develop an operational performance model with direct applicability to the post-9/11 US airline industry. The premise is to come as close as possible to ideal performance goals for five operational parameters i.e., on-time arrival, turnaround time, cascade delay percentage, taxi-out time, and taxi in time, based on length of haul. Since airline-operating performance depends upon several factors that cannot be described in crisp terms, these data fit within fuzzy logic set descriptors and theory. A database of numerical scores is transformed into a fuzzy database, and then fuzzy probabilities are used to assess the belief that the scores fall within the desired range for each criterion. Industry data are used to compare individual airline carrier performance measurements to anticipated industry operational goals. Application potential for the model is not restricted, however, to this industry.