The article presents a reply by professor Ronald V. Hartley on criticisms over the use of quadratic programming in a case of joint production. One of the cases in the author's article, "Decision Making When Joint Products Are Involved," entailed the possibility of producing a product in excess of demand. One of several alternative "uses" of this excess was to consider the creation of more demand by lowering the price. By defining some variables differently it is also possible to simplify the model. Once the optimal price and quantity have been achieved it would not be desirable to reduce the price just so that excess capacity is consumed. To do so would generate less revenue than setting a larger price with a smaller quantity sold. However, it would never be desirable to consider prices lower than the optimal since the revenue that could be generated by selecting a lower price could also be generated by selecting a higher price. At that higher price the quantity sold would consume fewer or equal resources.

As has been demonstrated, the process of deciding whether or not to produce beyond the split-off point is not as simple as set forth in most managerial accounting books. Linear programming can be used in these situations as long as the production relationships remain relatively constant. However, in applying linear programming it is necessary to allow for inventories of unused intermediate outputs or optimality may not be truly found. It is not possible to construct a general model, but a wide variety of assumptions have been discussed in this paper with the goal of establishing a methodology of formulating decision models when joint products are involved.

In summary, there are three basic observations: 1. If an input has a variable effect on the objective equation values and if it is fully consumed, then the basis will not change as long as its cost does not increase by an amount greater than the input's shadow price. The lower limit on the cost of such an input is-∞. If an input has the variable effect mentioned in #1 but is not fully consumed, then the limits of the change in its cost is given by the limits of the change allowed in the objective equation value of its corresponding slack variable. Thus, it may be desirable to include a known non-bottleneck resource in the model to determine the sensitivity of its cost. 3. In the set of all fully consumed inputs, simultaneous changes within the respective ranges will not cause the sensitivity analysis to be invalid.

The article focuses on the relation of accounting and operation research (OR). There has been an unresolved disagreement in the accounting profession as to the accountant's role in OR. In this article the author attempts to clarify what is meant by operations research and examines what are felt to be the logical implications of OR for the accounting profession. There are several phrases which have been used frequently in attempting to define OR. The author defines it as the scientific method to the development of predictive models which describe the stable patterns of order underlying certain business operations and thereby enabling the provision of quantitative information which is helpful in solving executive-type problems. There are three basic views which currently prevail concerning the relationship of accounting and operations research. At one extreme is the view that OR is entirely subsumed by accounting. On the other extreme is the view that accounting is relatively unaffected by OR. The third view is the middle-of-the-road view.

Reviews the book `Performance Measurement for World Class Manufacturing: A Model for American Companies,' by Brian H. Maskel.

Reviews the book "Accountants' Cost Handbook," 3rd ed., edited by James Bulloch, Donald E. Keller and Louis Vlasho.

Reviews the book "Accounting Principles: A Multimedia Program," by Dudley W. Curry and Robert J. Frame.