Optimal Purchasing Strategy Research Articles

Market Share for Semen and Cloned Embryos in Dairy Herds

Use of cloned embryos from desirable genotypes (commercial clone lines) enables faster dissemination of superior genetics to dauy producers. Under optimal purchasing strategies of milk producers, the annual proportion of replacement cows from commercial clone lines indicates the market share of cloned embryos compared with semen. Relevant factors affecting this market share were studied. To produce the next generation of commercial cows, the nucleus provided semen from the best sire and cloned embryos from the best female genotype, were selected for a trait associated with lactation. A commercial cow was inseminated if net returns from her expected offspring were higher than net returns from an expected contemporary clone. Net returns equaled net milk returns minus the costs required to breed an expected offspring. If not inseminated, a cow could be used for implantation of a cloned embryo. An increase in the genetic difference between cloned embryos and semen or in the annual additive genetic response achieved in the nucleus increased market share of cloned embryos. Market share decreased as the difference in costs required to breed an offspring from AI or implantation increased. In addition, market share was affected by characteristics of the commercial cow population before the introduction of clones.

The effect of extended payment terms on purchasing decisions

In this paper, computer algorithms are developed for determining optimal purchasing strategies for firms that receive extended payment terms from vendors. The resultant models are particularly attractive in high-volume environments with extended payment terms, a high opportunity cost of capital, and relatively large inventory-related costs. It is shown that purchases of quantities smaller than the traditional Economic Order Quantity may be appropriate in these environments and other situations where vendors offer either all-units or incremental purchase price discounts. The resultant algorithms are solved in this study using a spreadsheet-based search procedure implemented on a personal computer.

Stockpiling under price uncertainty and storage capacity constraints

A model is presented for the operation of an agent whose responsibility is to purchase and perhaps stockpile sufficient quantities of a certain commodity in order to satisfy an exogenous constant demand per time period. This is the situation faced by a state agency which is responsible for the purchasing of oil products to satisfy the demand of a country in which demand for energy has stabilized at a certain level. An important feature of the model is that the price of the commodity is described by a stochastic process. This reflects the volatility of prices of oil products. Furthermore, the model takes storage capacity constraints explicitly into account, and thus can help to assess the optimal level of storage capacity expansion. The relevant stochastic dynamic programming equations are derived and solved for the least cost function, which turns out to be piecewise linear in the inventory level. The storage capacity enters only in the computation of the constant term of the value function. The solution of the dynamic programming equation leads also to the optimal purchasing strategy of agencies with different levels of flexibility in their policy: in the model, an agency can be allowed or not to resell from the stock and it can have finite or infinite storage capacity.

A Decision Methodology for Coal Inventory Optimization

The procurement of coal as a power plant fuel is formally treated in a decision analysis methodology to assess optimal purchasing and stockpiling strategies. The uncertainties which characterize future prediction of fuel burn, receipt, and market price are expressed in a probabilistic multiperiod inventory model. A dynamic programming optimization procedure of the decision problem is developed. The effects of forecasting uncertainties and price variations are examined in several case studies and the value of perfect information is estimated.