Forecasting for Marketing

Research on forecasting is extensive and includes many studies that have tested alternative methods in order to determine which ones are most effective. We review this evidence in order to provide guidelines for forecasting for marketing. The coverage includes intentions, Delphi, role playing, conjoint analysis, judgmental bootstrapping, analogies, extrapolation, rule-based forecasting, expert systems, and econometric methods. We discuss research about which methods are most appropriate to forecast market size, actions of decision makers, market share, sales, and financial outcomes. In general, there is a need for statistical methods that incorporate the manager's domain knowledge. This includes rule-based forecasting, expert systems, and econometric methods. We describe how to choose a forecasting method and provide guidelines for the effective use of forecasts including such procedures as scenarios.forecasting, marketing

Audit and security issues with expert systems;

https://egrove.olemiss.edu/aicpa_guides/1016/thumbnail.jp

Fuzzy FMECA analysis of radioactive gas recovery system in the SPES experimental facility

Selective Production of Exotic Species is an innovative plant for advanced nuclear physic studies. A radioactive beam, generated by using an UCx target-ion source system, is ionized, selected and accelerated for experimental objects. Very high vacuum conditions and appropriate safety systems to storage exhaust gases are required to avoid radiological risk for operators and people. In this paper, Failure Mode, Effects, and Criticality Analysis of a preliminary design of high activity gas recovery system is performed by using a modified Fuzzy Risk Priority Number to rank the most critical components in terms of failures and human errors. Comparisons between fuzzy approach and classic application allow to show that Fuzzy Risk Priority Number is able to enhance the focus of risk assessments and to improve the safety of complex and innovative systems such as those under consideration

Quantification of Risk for USAF Fire and Emergency Services Flights as a Result of Shortage in Manpower

The United States Air Force (USAF) is currently experiencing a period of high operations tempo and overseas deployments have become frequent. These deployments will leave home installations short manned. Some amount of risk is incurred by the home installation as a result of the short manning. For an organization, such as an USAF Fire and Emergency Services (FES) flight, whose primary responsibility is the protection of life and property, the incurred risk could be catastrophic. Still no attempt has been made to quantify risk in terms of manpower for the USAF FES flights. The primary purpose of this research was to develop and validate a methodology to quantify risk in terms of manpower for FES flights. This research develops a decision tool to provide insight to FES Fire Chiefs on the risk associated with specific manpower decisions. The methodology was validated using data from Dyess Air Force Base FES flight. A secondary goal of the research was to determine a cost/benefit relationship between the risk level and the cost to backfill deployed firefighter positions with contract labor. The result was a decision tree model and pareto optimal graphs for the risk to manpower level and the cost/benefit relationship

Eligibility of External Credit Assessment Institutions

The Basel Committee on Banking Supervision in 1999 issued a draft New Basel Capital Accord (Basel 2). Its principles are to be incorporated into the European legislation and into the Czech banking regulations. The Standardised Approach to calculating the capital requirement for credit risk is newly based on external credit assessments (ratings). Banking regulators and supervisors have to be prepared for the process of determining eligible credit assessment institutions (ECAIs) and will have to elaborate a formal recognition procedure. This paper investigates the approaches a supervisor may apply to ECAI recognition and elaborates on the criteria of recognition. First, the paper reviews the available rating agencies on the market (including their rating penetration on the Czech market), their best practices and the experience with the use of their ratings for regulatory purposes. Second, drawing on international experience and the proposed Basel 2 rules, we outline the fundamental supervisory approaches to recognition, including the legal aspects thereof, and analyse their pros and cons and the frontiers of supervisory decision making. Third, we outline the rules for recognition, including requirements or expectations (e.g. soft limits), documentation and typical interview questions with the potential candidates. We find the CNB's approach to be in compliance with CEBS Consultative Paper CP07 (issued for public consultation in June 2005).Basel capital accord, Basel II, Credit rating, default, eligibility criteria, eligibility evaluation, external credit assessment institution (ECAI), export credit agency (ECA), mapping rating grades, market acceptance of ECAIs, rating agency, recognition process

Forecasting for Marketing

Research on forecasting is extensive and includes many studies that have tested alternative methods in order to determine which ones are most effective. We review this evidence in order to provide guidelines for forecasting for marketing. The coverage includes intentions, Delphi, role playing, conjoint analysis, judgmental bootstrapping, analogies, extrapolation, rule-based forecasting, expert systems, and econometric methods. We discuss research about which methods are most appropriate to forecast market size, actions of decision makers, market share, sales, and financial outcomes. In general, there is a need for statistical methods that incorporate the manager\u27s domain knowledge. This includes rule-based forecasting, expert systems, and econometric methods. We describe how to choose a forecasting method and provide guidelines for the effective use of forecasts including such procedures as scenarios

Design of a Process to Implement an Annual Community Fundraiser for Sprout Up

Sprout Up—in San Luis Obispo—is currently a non-profit organization that teaches free environmental science education to first and second graders around the San Luis Obispo area. Due to recent circumstances, Sprout Up will soon be losing its non-profit status, and therefore they will be losing their funding as well. Their problem is that they need a stable annual fundraiser that will help the organization continue to provide free services to local elementary schools as a Cal Poly club rather than a non-profit. Multi-criteria analysis techniques were used to determine what would be the best solution to their problem and found that hosting an annual Gala would be the most beneficial. Using Industrial Engineering tools, a design was created for a process to implement the annual community fundraising event for Sprout Up. The Gala prototype event was held at Santa Rosa Park on May 13th from 3pm to 7pm, and raised roughly $1,200 for the organization. The recommendation is to continue to perform the event annually with changes to the facility layout and starting the project earlier to give more time for fundraising

Pooling of prior distributions via logarithmic and supra-Bayesian methods with application to Bayesian inference in deterministic simulation models, The

1998 Summer.Includes bibliographic references.Covers not scanned.Print version deaccessioned 2022.We consider Bayesian inference when priors and likelihoods are both available for inputs and outputs of a deterministic simulation model. Deterministic simulation models are used frequently by scientists to describe natural systems, and the Bayesian framework provides a natural vehicle for incorporating uncertainty in a deterministic model. The problem of making inference about parameters in deterministic simulation models is fundamentally related to the issue of aggregating (i. e. pooling) expert opinion. Alternative strategies for aggregation are surveyed and four approaches are discussed in detail- logarithmic pooling, linear pooling, French-Lindley supra-Bayesian pooling, and Lindley-Winkler supra-Bayesian pooling. The four pooling approaches are compared with respect to three suitability factors-theoretical properties, performance in examples, and the selection and sensitivity of hyperparameters or weightings incorporated in each method and the logarithmic pool is found to be the most appropriate pooling approach when combining exp rt opinions in the context of deterministic simulation models. We develop an adaptive algorithm for estimating log pooled priors for parameters in deterministic simulation models. Our adaptive estimation approach relies on importance sampling methods, density estimation techniques for which we numerically approximate the Jacobian, and nearest neighbor approximations in cases in which the model is noninvertible. This adaptive approach is compared to a nonadaptive approach over several examples ranging from a relatively simple R1 → R1 example with normally distributed priors and a linear deterministic model, to a relatively complex R2 → R2 example based on the bowhead whale population model. In each case, our adaptive approach leads to better and more efficient estimates of the log pooled prior than the nonadaptive estimation algorithm. Finally, we extend our inferential ideas to a higher-dimensional, realistic model for AIDS transmission. Several unique contributions to the statistical discipline are contained in this dissertation, including: 1. the application of logarithmic pooling to inference in deterministic simulation models; 2. the algorithm for estimating log pooled priors using an adaptive strategy; 3. the Jacobian-based approach to density estimation in this context, especially in higher dimensions; 4. the extension of the French-Lindley supra-Bayesian methodology to continuous parameters; 5. the extension of the Lindley-Winkler supra-Bayesian methodology to multivariate parameters; and, 6. the proofs and illustrations of the failure of Relative Propensity Consistency under the French-Lindley supra-Bayesian approach

Combat Air Forces Campaign Level Modernization Planning-A Study in Group Decision Making

Modernization is a critical component of the current transformation effort within the Department of Defense (DoD) Effective and efficient modernization planning will provide for the improved allocation of limited funding. The Air Force currently conducts capabilities based modernization planning to identify shortfalls. Air Combat Command (ACC) utilizes multi-objective decision analysis (MODA) techniques to support the modernization planning process (MPP). A MODA model has been created to identify and quantify capability shortfalls across a diverse range of mission areas. Groups of subject matter experts are utilized to provide model inputs improving the usefulness and credibility of the model. The intent of this research effort is to document the ACC modernization model and provide insight into their use of groups