John P. Matthews

Insurance Exposure and Investment Risks: An Analysis Using Chance-Constrained Programming

By exposing itself to greater risks on the invested funds, the company can also increase its expected returns. Because there are risks involved with both the insurance and investment operations, a major problem for a company is to determine just how the two types of risks are to be balanced. This paper formulates the premium-volume/investment-mix problem for nonlife-insurance companies as a chance-constrained programming problem. A number of numerical examples of the model were run to assess the effects of investment risks, insurance risks, and the willingness of the company to tolerate risks. The paper derives the conclusion that risks in both investment possibilities and insurance portfolios are compensated for, to a large extent, by varying the growth rate of premiums, in addition to shifts in portfolio makeup. The two problems-growth and portfolio selection-cannot and should not be separated. The paper shows that the traditional rules of thumb-e.g., if the premium-surplus ratio is high, the common-stock portion of the investment portfolio should be low-are somewhat ambiguous and not borne out by the numerical examples.

Optimal lot sizing for the process cycling problem

Traditional lot sizing models seek to strike a. balance between set-up costs and holding coats for the single-machine or process cycling problem. These models require that the process somehow has achieved the optimal aggregate inventory and item inventory levels. Furthermore, most lot sizing models require that aggregate demand equals aggregate production. The model presented in this paper generates production schedules which allow for item inventory imbalance and inventory growth.

The Focus, Span and Links in Research on Operations Management Strategy

A review of Operations Management Strategy (OMS) research shows that a spectrum of issues has already been empirically and theoretically investigated. Collectively, while OMS research efforts have considered many issues, individual research efforts have been more narrowly defined. With the absence of an established framework to place important OMS research, this body of literature takes the appearance of a collection of loosely related efforts. This proposed framework lends additional structure to the field and allows the identification and evaluation of “hot” and “cold” areas of research interest in both management and operations management.

Equipment dedication and automation: Some empirical findings

Automation has long been viewed as a vehicle by which one could capture improvements in productive efficiency. In many situations, however, this efficiency was attainable only through increasing focus and dedication of the productive process to the support of a relatively narrow set of products. During the last decade, attention has turned to two other approaches. One was the employment of non-automated but yet dedicated equipment to select product groups. The other was the employment of highly sophisticated, automated equipment in an essentially non-dedicated environment. The former we refer to as reflective of a Japanese approach, the latter a FMS. Our study focuses on the degree to which these approaches are reflected in the US manufacturing industry and their relationship to economic performance measures. The findings suggest that movement from a traditional high dedication, high automation position toward either a Japanese or a FMS posture should be carefully evaluated since US firms have tended to compete more effectively when employing the more traditional approach.

Insurance exposure and investment risks: Management and regulatory implications

By exposing itself to greater risks on invested funds the company can also increase its expected returns. Because there are risks involved with both the insurance and investment operations, a major problem for an insurance company is to determine just how the two types of risks are to be balanced. In an earlier paper the authors dealt with the relationships among growth in premiums written, insurance loss and investment return. In this paper the questions are revisited from a policyholder and regulatory point of view, and suggestions are made concerning the reaction of the firm under various postures the management may take towards risk.

Inventory replenishment rules for systems exhibiting ‘on-off’ stochastic demand patterns

Optimal replenishment rules are presented for determining both the best stock-up level and the best time to receive an order in a fixed order-interval inventory system subject to stochastic demand. In developing such rules, an important but so far neglected factor is taken explicitly into account, namely, the portion of the period during which demand cannot possibly occur. It is shown that replenishing the system at the right time is far more important than replenishing the system up to the right level. The findings have special importance for review of just-in-time policies which may inappropriately (in this context) suggest timing replenishment so as to coincide with commencement of demand.