Many US corporations are moving increasingly towards the integration of computer-based automation equipment in efforts to retain or restore their competitive edge in domestic and world markets. The tremendous interest in FMS has expectedly drawn focus and research on analytical techniques that can be used to analyze such investments. A survey of the literature on FMS evaluation reveals the widespread realization that such investments cannot be justified using traditional discounted cash flow techniques such as net present value, internal rate of return or payback. While the inadequacy of these traditional justification procedures has become apparent, much of the published literature has focused on merely documenting the inability of the DCF procedures to consider the intangible or strategic benefits of FMS investments. There have been few attempts to provide an explicit solution to this crucial issue.

FMS Justification :

One common theme in the literature on FMS justification is that traditional DCF procedures alone are inadequate to evaluate FMS investments. The widely accepted notion is that FMS investments are strategic investments and their full impact on the firm is not estimable in terms of cash flows. Case studies of the implementation of FMS's have found the impact to be pervasive throughout the manufacturing organization. Results reported in review articles indicate significant reductions in inventory, throughput times, and space requirements with concurrent increases in quality, product variety and volume flexibility. These changes must be translated into cash flows in order to apply discounted cash flow (DCF) techniques.

Four issues must be overcome if DCF methods are to be employed in the evaluation of FMS alternatives. First, the cash flows resulting from improvements in quality, for example, must be measurable. Second, the impact of such improvements in revenue as well as costs must be identified explicitly. Third, the increased contributions provided by these systems over extended periods of time due to their enhanced capabilities must be estimated as well. Finally, the ability to exploit these improvements by other functions (such as marketing) must be gauged. A further deficiency in current applications is the evaluation of the "do not implement" alternative. If these systems provide significant benefits and are adopted by competitors, the payoff from not adopting them will be negative and not zero.

Several authors (Myers, 1984 and Logue, 1981) have suggested that the growth of opportunities associated with strategic investments be interpreted as options on the underlying assets analogous to call options on securities. The use of option pricing theory is, therefore, recommended. There are, however, three major problems with the option pricing approach for the evaluation of an FMS. First, growth options on capital projects have no precisely known exercise dates. Second, the proposed analytical approach implicitly assumes that the impact of a growth option can be measured in terms of cash flow. However, this measurement problem is precisely the one which renders the standard DCF procedure inappropriate. Third, the option pricing approach requires that the influence of several strategic factors be somehow aggregated and expressed in terms of the project's growth options.

Michael and Millen (1985) suggested a mixed approach. If the proposed automation project fails to clear the firm's hurdle rate, then a non-quantitative strategic justification approach must be employed. To do so, the non-cash flow benefits are compared to the required rate of return not covered by cash flow benefits. If the decision makers believe the benefits are sufficient, the alternative is selected. However, such an approach does not recognize that all projects that clear the hurdle rate need not be strategically desirable and does not indicate how to select amongst strategic investments that clear the hurdle rate.

Furthermore, while the input of experienced executives must be included in these processes, informal judgmental systems have a less than perfect memory and, therefore, pose serious problems of inconsistency. This inconsistency problem may lead to a decision maker reaching a different conclusion on the same FMS in different time periods without any new information. The same manager's decision across projects may not be consistent. An appropriate framework to evaluate FMS alternatives must provide for a systematic consideration of strategic and other qualitative factors that cannot be expressed in cash flow terms. This can be done by relying on formal and robust methods of analyzing qualitative data. This framework must also be capable of integrating the results of such an evaluation with the estimable cash flows relating to an FMS.

Evaluating Qualitative Factors in FMS Justification :

Multiple Attribute Decision Models (MADM) enable the decision maker to systematically assess qualitative information . MADM is best suited for problems which involve the selection of an alternative from a relatively small list of alternatives as opposed to multiple objective decision models (MODM) which are most suited for selection of an alternative from an infinite set of options implicitly defined by constraints. Since the number of strategic projects is likely to be finite, MADM are clearly more suitable for assessing them.

The Analytic Hierarchy Process (AHP) is an approach to systematically evaluate often conflicting qualitative criteria. Like other MADM, the AHP also attempts to resolve conflicts and analyze judgments through a process of determining the relative importance of a set of activities, players or criteria.

The AHP is comprised of three principal component processes that start by decomposing the principal problem into a hierarchy. Each level consists of a set of elements and each element, in turn, is broken into sub-elements for the next level of the hierarchy. The final level consists of the specific courses of action that are being evaluated for adoption. Within each hierarchical level, priorities are being evaluated for adoption. Within each hierarchical level, priorities are established using a measurement methodology. Finally, the third major component of the AHP is a measurement theory that establishes the priorities of the hierarchy and the consistency of the judgmental data. The basic assumption underlying the measurement methodology in AHP is that relative dominance can be measured by pairwise comparisons. These comparisons are performed across the set of n attributes to establish their relative weights.

The weights are estimated from data or from experienced decision makers or a group of experts. A pairwise reciprocal matrix is used to represent the relative dominance of each element in a particular level over other elements in the level with respect to each of the elements in the immediate higher level of the hierarchy. The principal eigenvector of this matrix is then derived and weighed by the priority of the element in the higher level with respect to which the evaluation has been made. This process of eigenvector extraction and prioritization by weighting leads to a unidimensional priority scale for the elements in each of the hierarchical levels. The measurement scale used in AHP ranges from 1 (elements i and j of equal importance) to 9 (element i is absolutely more important than element j).

The next step in the process is to obtain a measure of the consistency of the judgmental data. If we have a consistent matrix with unit rank and given any one row of the matrix with n entries, the remainder of the matrix can be derived. In general, a consistency ratio of 10% or less is considered very good. If consistency is poor, additional data or another round of comparisons may be required.

Integration of Qualitative Evaluation with DCF Techniques :

In the case of many FMS, it is possible to estimate cash flows even though the impact of strategic factors may not be reflected in such estimates. In such cases, the project can be evaluated along with the strategic dimensions using the AHP and the results can be explicitly integrated with the project's estimated cash flows. The alternative courses of action in the AHP model will be the decision to accept the alternative projects. The weights will, therefore, indicate the strategic desirability of an FMS alternative relative to other available FMS alternatives. The relative strategic net present value (SNPV) can be computed for each alternative i as the relative strategic weight for alternative i (wi) times the total present value of cash flows for alternative i (PVCFi ) minus the net investment for alternative i (NINVi), in present value terms.

Projects can be ranked according to their strategic NPV and the best project(s) can be chosen. Note also that the weights can be different for each year of project life, if necessary. Furthermore, the overall desirability of the alternatives can be monitored by computing the overall FMS desirability index (FPDI) which is given by expressing the strategic NPV over time as a ratio of the initial strategic NPV. The FDPI can also serve as a pointer for considering abandonment or divestment options.