The Selection and Management of Cost Justification Techniques among Advanced Manufacturing Technology Companies in Malaysia
Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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