Kristin Kennedy

A small company's dilemma: using search engines effectively for corporate sales

Purpose – Smaller companies must continually review the pay‐per‐click (PPC) option or an organic listing on search engines. The purpose of this paper is to present a case study of a small manufacturing firm that is beginning to evaluate which search engine, Yahoo or Google, is more cost effective. Ultimately, management would like to identify if PPC advertising is worth the cost to a small company.Design/methodology/approach – A one months section of data from Yahoo and Google was examined. Patterns or indications as to which key word landed a better bid position was determined. Seven consecutive campaigns for click‐through rates (CTRs), average cost per click (CPC) and average position of keywords between the search engines Yahoo and Google were observed.Findings – The average CPC was higher with Google. Kennedy Incorporated set a budget with Google and Yahoo to stay below a certain dollar limit, thus the total costs were the same. That would make the difference in the average CPC rather significant. Ma...

A Collaborative Project to Increase the Participation of Women and Minorities in Higher Level Mathematics Courses

In this article, the authors describe a program developed to encourage women and minorities to continue their study of mathematics in high schools until graduation. The 3- year program was a collaborative effort by professors and students from Bryant University, local businesses, and local high schools. During the 3 years, the program evolved from the development and presentation of reality-based mathematical modules taught in the high school classrooms to an interdisciplinary enrichment activity at Bryant University. The university students acted as mentors or tutors to the high school students. Throughout the program, the business representatives, university personnel, and high school teachers collaborated to bring mathematics alive to the students through real-world applications.

Multiple Sourcing Alternatives Using Nearly Optimal Programming

Abstract A linear programming (LP) model representing multiple sourcing is investigated. The LP represents the allocation of order quantity for three potential vendors. The optimal solution uses two of three vendors, with 75% of the order being supplied by the second vendor and the remaining 25% by the third. Nearly optimal programming (NOP), a form of sensitivity analysis not generally covered in management science or operations research texts, is applied to the LP model. We show that with only minor modifications to the model, a variety of new solutions can be generated that represent distinct solutions from the optimal. These distinct solutions provide the manager and the business student with alternatives, which could be useful if there are problems with specific vendors.

Forecasting Patient Volume for a Large Hospital System: A Comparison of the Periodicity of Time Series Data and Forecasting Approaches

Managing a large hospital network can be an extremely challenging task. Management must rely on numerous pieces of information when making business decisions. This chapter focuses on the number of bed days (NBD) which can be extremely valuable for operational managers to forecast for logistical planning purposes. In addition, the finance staff often requires an expected NBD as input for estimating future expenses. Some hospital reimbursement contracts are on a per diem schedule, and expected NBD is useful in forecasting future revenue.Two models, time regression and autoregressive integrated moving average (ARIMA), are applied to nine years of monthly counts of the NBD for the Rhode Island Hospital System. These two models are compared to see which gives the best fit for the forecasted NBD. Also, the question of summarizing the time data from monthly to quarterly time periods is addressed. The approaches presented in this chapter can be applied to a variety of time series data for business forecasting.

Honing a Predictive Model to Accurately Forecast the Number of Bed Days Needed to Cover Patient Volume for a Large Hospital System

Abstract Forecasting the number of bed days (NBD) needed within a large hospital network is extremely challenging, but it is imperative that management find a predictive model that best estimates the calculation. This estimate is used by operational managers for logistical planning purposes. Furthermore, the finance staff of a hospital would require an expected NBD as input for estimating future expenses. Some hospital reimbursement contracts are on a per diem schedule, and expected NBD is useful in forecasting future revenue. This chapter examines two ways of estimating the NBD for a large hospital system, and it builds from previous work comparing time regression and an autoregressive integrated moving average (ARIMA). The two approaches discussed in this chapter examine whether using the total or combined NBD for all the data is a better predictor than partitioning the data by different types of services. The four partitions are medical, maternity, surgery, and psychology. The partitioned time series would then be used to forecast future NBD by each type of service, but one could also sum the partitioned predictors for an alternative total forecaster. The question is whether one of these two approaches outperforms the other with a best fit for forecasting the NBD. The approaches presented in this chapter can be applied to a variety of time series data for business forecasting when a large database of information can be partitioned into smaller segments.

The Evolution of an Undergraduate Actuarial Mathematics Program

Abstract Bryant University was originally a school for business majors and offered only a few mathematics courses. After becoming accredited by the New England Association of Colleges and Universities in the 1960s, the college was required to upgrade its offerings in the area of mathematics. In the 1970s, the department offerings were increased to include computer electives, advanced topics in mathematics, and graduate-level quantitative business courses. As the department grew, majors were established in the department, including a major in Actuarial Mathematics. This major has grown from its first graduating class in 1986 of five students to a current total student enrollment today of 172. This paper discusses the evolution of the major and the success of the program. We provide the curriculum and highlight changes, which were necessary to conform to the ever-changing requirements for becoming an actuarial Fellow. We also discuss unique aspects of our program such as the SAS Certification in Data Mining that has greatly increased the marketability of our students.