Strategic Models Based on Management and Accounting - Dissertation Example

Introduction Strategic models can be overly complicated or they can be aimed toward practicality. Each type of project requires its own model. General management and accounting require simpler models than engineering literature, which requires a very mathematically complex model for accurate analysis. New manufacturing technology projects require new strategic models, or perhaps new ways of employing traditionally accepted methods. Strategic Models Based on Management and Accounting In his 1971 survey, Bromwich argues that the attitudes of management the choice of models brought to bear on capital budgeting evaluations. Certain non-quantifiable factors must be taken into account for accurate evaluations. One of his examples shows that if a project with a net present value of £200 is rejected on the basis of a non-quantifiable factor, that non-quantifiable factor becomes quantifiable at £200. In addition, if a model produces a negative NPV but a non-quantifiable factor with a positive value is introduced, that figure could wipe out the negative value and make the project acceptable. Management must be aware of these situations, and take all factors into consideration. The model introduced by Airey and Young (1983) holds that strategic factors take precedence over financial factors; in other words, projects should be evaluated and accepted on the basis of sound strategy rather than predictable financial factors. The over-all scheme of capital budgeting only involves financial data in a supporting role. Airey and Young also identify three main characteristics of Advanced Manufacturing Technology projects: A. flexibility; B. higher levels of risk; and C. the utilization of Flexible Manufacturing Systems which cannot be evaluated solely upon financial factors. Thus, traditional capital budgeting techniques are poor methods for new technology projects involving capital investment; new techniques must be developed. On the other hand, Kaplan (1986) argues that if a company considers only strategic motivations and continually invests in projects with bad financial returns, it will of course go bankrupt. Kaplan comments that conventional DCF models based upon available data should be employed. If a new technology project shows positive net present value it should be accepted. Kaplan introduces the caveat that a DCF analysis may be mathematically complex; the proper discount rate and all related alternatives must be introduced into the analysis of new technology projects. Traditional techniques such as the DCF should not be ignored; traditional techniques should be modified to meet the evaluation demands of different projects. Management must weigh all benefits, both tangible and intangible. If there are large differences, the project should be rejected; if intangible benefits cancel out or outweigh tangible benefits, the new technology project should be acceptable to management. The evaluation model he introduces compares intangible benefits to negative NPV. His Financial Appraisal method brings the company’s strategic factors together with financial returns; once a project is accepted alternative strategic benefits are set aside. If the project is accepted only because of financial considerations, strategic factors become moot. Kakati & Dhar (1991) concur with this assessment and add that a two level model might also be applicable, combining financial criteria with a careful analysis of strategic criteria. Thus strategic planning is included in the budget along with the benefits of new technology projects. Bromich and Bhimani (1991) take Kaplan’s ideas one step further and use a checklist to generate a final score profile of projects under consideration. Traditional financial appraisal techniques tend to ignore strategic or non-quantifiable factors. A matrix is used to weigh considerations and to arrive at a complete overview of a project and this matrix attempts to measure all factors and benefits in relation to the new technology project. Non-financial benefits are assigned a score and included in the model with the known financial benefits. Thus, non-quantifiable benefits receive quantification. Their model includes quantitative analysis alongside an informal evaluation of strategic benefits in order to determine the most accurate prediction of long-run strategic profit. This overall view provides a quantifiable score which can be applied to the evaluation of all factors relating to the project. By employing this model, managers take more control of the evaluation rather than allowing accountants to perform the complete evaluation of complex projects. Finally, Bromwich and Bahaman identify three process elements which contribute to the efficacy of their model: A. monetary units are expressed directly; B. monetary units are used to quantify items; and C. non-financial items are scored appropriately. If more than one project is under consideration, the Bromwich and Bhimani method provides a point system to effectively analyze which project should be accepted. Samuels (1991) introduces a three-stage model which can be employed to evaluate strategic projects. In the first stage of his model, projects are brought in line with overall corporate strategy, and if the project does not pass this assessment it is summarily rejected. Projects which fit the overall corporate strategy move on to the second stage of evaluation which employs the net present value model. The discount rate may be adjusted at this stage, at the manager’s discretion, if non-quantifiable benefits warrant the adjustment. Projects which show a positive NPV are accepted; those which show a negative NPV move on to the third stage of evaluation. At this point, projects are evaluated further and if intangible benefits outweigh the negative NPV, the project is accepted. Regarding AMT projects, Meredith and Suresh take issue with poorly defined strategic areas in new technology proposals. Three types of AMT projects (stand-alone, linked and integrated) call for different justifications. Stand-alone projects may use standard evaluation models, namely PB, IRR, NPV and so on. Linked AMT projects require value analysis, portfolio analysis, and risk analysis to account for various factors. Integrated projects are more complex and require the additional evaluations of company objectives, R&D considerations, competitive advantages, and the technical importance of the proposed project. Pilot studies provide data and the opportunity for further analysis before fully accepting a project. The three phases of the Meredith and Suresh model are similar to McDonald (1985), with advanced machinery introduced at the first stage. The benefits of advanced machinery can be quantified and predicted in financial terms, and the model should not be difficult for either production engineers or accountants to apply. In the second stage, computer numerically controlled machines are linked. By the third stage of the evaluation, production efficiencies should be achieved by the fully computerized manufacturing system. McDonald further adds that benefits which are not easily quantifiable, such as fast delivery times and quick response to customer demands (both of which increase customer satisfaction) become important benefits; thus, company efficiency should be quantified and included in the analysis. Pragmatic Models Based on Engineering Sarkis has been instrumental in a great number of studies (c.f. Sarkis, 1995; Sarkis and Lin,1994; Sarkis and Semple, 1994; Sarkis and Liles, 1995; Sarkis 1997). In general, these models prefer a strategic focus rather than simply considering operational and tactical considerations. This top down approach can be applied to computer integrated manufacturing systems. Managerial decisions identify general methods of evaluation and technology management in the context of manufacturing organizations using these technical models. Quantitative modeling is used alongside qualitative modeling, much as is occurring in the recent interest in the NPV and IRR tools. Multi-criteria, integrated approaches allow the analysis of factors which are typically difficult to quantify, such as product flexibility and opportunities for expansion and quality improvement. This scheme sets the appraisal into the general strategic process of decision-making. A data envelopment analysis works well for flexible manufacturing systems investments. It must be remembered that Sarkis is an engineering economist and so his theories generally come from a mathematical background. Given this, Sarkis’ models lean toward operations management and focus upon justifying and implementing process engineer projects. Although they approach evaluation from a different angle, these models contribute significantly to theory, though they are somewhat limited regarding non-AMT type investments. Project managers may use a two step approach to the justification process: QFD and IDEF0. The former refers to quality function development; in other words, research and development justification. The latter is an ICAM definition, with the 0 being a functional modeling technique. This two step approach accounts for future requirements from customers and forms a business case plan to handle those requirements. QFD may also employ SJET (Justification of Enterprise Technology) which links strategy links and operations links between the technology under consideration and the company’s overall strategy. The integrated approach also introduces such schemes as utility theory, weighting theory, and AHP into the mix to help managers make informed decisions. Corporate agility has become a new focus of recent research. Agility theory combines true flexibility with better operational function integration within the company. Concerns with agility create the need for new methods to analyze technology projects. Sarkis brings ANP to the table for more complex evaluations involving functional agility and other strategic considerations. “An agile enterprise is an enterprise whose processes are designed to respond effectively to unanticipated change” (Meade and Sarkis, 1999, p. 241). Huang and Ghandforoush (1984) propose an interesting model applied to selecting robots from among several vendors. This evaluation technique especially benefits senior managers because it uses a bottom-up approach to evaluation. The model involves five distinct steps. First, a robot project selection list is created from the choices available from the vendors. Second, evaluators look for factors which would eliminate a particular vendor. For instance, the vendor’s proposal may not measure up in the context of the capital budget, or the vendor’s product return policy may be lacking. Third, the evaluator creates an objective factor measure (OFM) for each vendor, which involves considering capital costs, equipment costs, etc. Typically, the evaluation reveals which vendor has the lowest net cost, and that vendor receives the highest OFM score. Using this methodology, all the vendors can be ranked accordingly. Fourth, the evaluator creates a list of subjective factors which are assigned a rating by management from 0 to 10. Further, the subjective factor weights are rated from “good” (4) to “poor” (1), refining the ranking system even more. These factors allow the evaluator to arrive at an objective view of the subjective factors for each vendor involved. The fifth and final step in the Huang and Ghandforoush model creates a performance measure for each vendor by combining the objective and subjective components measured during the previous steps. This performance measure allows the evaluator to truly see which vendor is preferable over the others. Very early in this model, projects which do not meet financial criteria are eliminated from consideration. Both objective and subjective (tangible and intangible) benefits are considered as well. One serious drawback of this model is that it is limited in the types of projects which can be considered; the robot selection illustration defines the limits of this five step process. This rather complex and time-consuming process provides managers and evaluators with several benefits at the end. At some point, nearly all important factors are taken into consideration, including costs and savings, rates of return, the company’s capital budget, and factors which are not as easily quantified such as subjective criteria. There is no indication in the article whether actual costs are figured in the calculations or whether discounts are applied at some stages, nor is it known whether all quantifiable costs are included or only specific ones. Nelson (1986) introduces a Flexible Manufacturing Systems (FMS) scoring model. The FMS incorporates traditional financial appraisals with a new model combining five sub-models. The five sub-models produce scores in technology assessment, equipment evaluation, capacity elasticity, cost-budget analysis, and adjusted NPV. The model is considered to be additive and not statistical, and the five sub-scores are added together to give the evaluator an overall score for the project as a whole. The method is very useful when applied to FMS projects such as modernization capital projects and replacement capital projects, but Nelson indicates the assumptions in the model may need to be adjusted to account for risk factors. The cost of capital is directly related to risk, and the NPV of independent projects contain certain risks which may necessitate adjustment. When multiple alternative courses are being considered, Sullivan (1986) employs a simplified scoring system to determine how alternatives compare against the criteria set forth. By assigning a criterion a level of desirability from -2 (least desirable) to +2 (most desirable), AMT projects can be efficiently evaluated. This Profile Chart model creates a picture of the strengths and weaknesses of the project and the linear additive model produces a final score. Managers may then choose the projects which have the highest scores in order to decide which projects will produce the desired result. One limitation to this model is that it cannot be applied as a general investment appraisal; the ratings use a system of measurement that is not transferrable. Engwall (1988) approaches the evaluation from an NPV standpoint, using financially quantifiable factors alongside non-quantifiable factors to determine an overall score. Performance indicators are turned into numerical scores and account for the strategic vision of the company. This Multiple Attribute Decision Model (MADM) uses three critical factors: A. financial quantitative; B. non-financial quantitative; and C. qualitative. The weighted average of each of these factors allows decision-makers to determine the most efficacious project. This MADM approach is flexible in that it can be applied to many types of projects, not just AMT projects; it has aspects of the Flexibility Manufacturing Systems (FMS) model as well, along the lines of Azzone and Bertele (1989). Grundy and Johnson (1993) introduce the idea of uncertainty (risk) which acts as a constraint during decision-making. Risk assessments also buffer managers from excess responsibility due to their decisions. Linking financial and strategic appraisals is not a straightforward process; a constellation of problems can arise, and risk assessment allows decision-makers to avoid unpleasant evaluations and decisions. This assessment model increases the flexibility of the post-project appraisal should the project not perform as initially predicted. Badiru, Foote and Chetpuzha (1991) suggest a simplified spreadsheet model which illustrates and compares the various attributes of the proposed project. Their Analytical Hierarchy Process (AHP) is designed with interactive macros. The fact that the model includes a computer-based tool means that managers can easily see the criterion scores and adjust numbers as needed to arrive at effective evaluations. The spreadsheet model may be easier for managers to use because most are familiar with using spreadsheets for a variety of other tasks. The Expert System (ES) approach suggested by Fisher and Nof (1987) brings practicality into the appraisal process by recognizing the unique decision-making capabilities of managers and experts in the economic analysis process. Strictly quantitative models sometimes ignore the human element, but the Expert System model allows for the manager to employ his or her own judgement as needed. Suresh (1991) approaches AMT evaluations from a similar angle. This model employs multiple objectives and fully supports the decision-making environment. Optimal implementation sequences can be introduced for CNC modules and precision tooling. This model also allows for a company’s current equipment and production situation and formulates future schedules to ease transitions, and fits well into a company’s overall strategic vision. Bibliography Airey, J., & Young, C. (1983). Economic justification - counting the strategic benefits. The 2th International Conference on Flexible Manufacturing Systems, K Rathmill (ed) (pp. 549 - 554). Kempston: IFS. Azzone, G., & Bertele, U. (1989). Measuring the economic effectiveness of flexible automation: a new approach. International Journal of production Research , 27 (5), 735 -746. Badiru, A., Foote, ,. B., & Chetpuzha, J. (1991). A multiattribute spreadsheet model for manufacturing technology justification. Computers and Industrial Engineering , 21 ((1 -4)), 29 -33. Bromwich, M. a. (1991). Strategic Investment appraisal. Management Accounting , 69 (3), 45 - 48. Bromwich, M. (1970). 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