The Selection and Management of Cost Justification Techniques among Advanced Manufacturing Technology Companies in Malaysia
Bibliographic record
Abstract
This paper investigates the selection and management of cost justification techniques on AMT among the manufacturers in Malaysia. Adoption of AMT involves huge amount of investment and a high degree of uncertainty, and therefore a considerable level of attention is required in a manufacturing firm at all level. As a result, issues involving planning, selection and justification procedures assume greater importance. In other words, instead of rushing to invest in AMT, a firm should re-evaluate the objectives, strength and weakness in order to compete in the domestic as well we international manufacturing market. Introduction Manufacturing practices and processes have come under increased pressure from global competition. Demands for improved customer service, breadth of product line, improved quality, quick response and much shortened time-to-market for new product introduction cannot be ignored by firms. In the face of these intense pressures, manufacturers around the world are moving away from mass production processes to innovative customisation. They are turning to greater flexibility, cost cutting and speed in manufacturing practices. Adoption of new technologies is a key element to a firm's success with proper costing management (Small, 1999). The Survey of Literature A critical issue in implementing AMT is justifying the investment in the new technology. This is a key decision-making point in the implementation process. Failure to meet the plant's investment justification criteria for AMT projects will signal the demise of the automation project. Many articles have been written over the last decade on the inappropriateness of traditional economic justification techniques for evaluating advanced manufacturing technology (Burcher and Lee, 2000). Economic models, also referred to as traditional or accounting model fall into two categories: basic model and the discounted cash flow (DCF) models. These economic models have historically been the primary method for AMT evaluation (DeGarmo et al, 1984; Fabrycky & Thuesan, 1980). The development of a methodology that evaluates strategic benefits of AMT investments and integrates them with traditional economic analysis will accelerate the acceptance of AMT investment and improve the industrial competitiveness (Low et al., 2001). The focus of the strategic evaluation of AMT investments is to enhance the manufacturing competitiveness by directing resources toward changes which improve critical manufacturing capabilities. Most detractors of the traditional techniques suggest that they cannot account for all of the benefits of AMT and fail to meet the industry requirement for economic justification when applied to these technologies. Canada and Sullivan (1990) identify what they address as the 'persistent pitfalls' of traditional techniques. The main pitfalls of all these traditional methods are identified as the arbitrarily high setting of cost of capital figures and required rate of return or hurdle rates and insufficient benefits analysis. Research Methodology The population for this study is lor all manufacturing firms with a total asset of at least RM50 millions from the Federation of Malaysian Manufacturers (FMM) Directory. This study is an on-going research to investigate various areas in the manufacturing sector in Malaysia. Questionnaires are addressed to Company Accountants, Accounts Managers, Head of the Engineering Department and Manager of the Engineering Department. A total of 120 responses were obtained from the 250 survey schedules mailed to prospective respondents. The two mailings resulted in a total of response of 120 returned questionnaires. Seventeen of the 120 responses were classified as unusable for various reasons, including excessive missing data and failure to provide essential data. Analysis and Findings Hypothesis 1 (i, j) Industry does not use justification technique i (or a greater percentage of technology investment decisions than justification technique j. …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".