Tuesday, May 5, 2020
Business Decision Analysis Manufacturing IndustriesÃ¢â¬Free Samples
Question: Discuss about the Business Decision Analysis for Manufacturing Industries. Answer: Introduction Manufacturing industries have their unique model of working based on various environmental factors. These models help in smooth functioning of any organisation. Decision making models helps to take various decisions of the organisations and these models solve various problems in various situations. These models depend on the environmental or external factors of the industry. Decisions are taken for the manufacturing industries on considering the environmental or external factors of the industry. Apparel manufacturing industries in Australia face various constraints regarding the material used, the price of the materials and their quality (Power et al., 2014). Decisions making models are set up in this context, which helps the manufacturing companies to decide about the contents of the apparels. Literature review The apparel manufacturing companies of Australia have various models in their course of business. These models include various environmental or external factors that have an impact on their business. Decision tree analysis was usually done on these models, this analysis helped the apparel manufacturing companies to have a smooth functioning of their business, and they earned more profit in the business. Considering the environmental or external factors, the appeal manufacturing industry of Australia decided the type of product they would use for the manufacturing of the products (Yager Kacprzyk, 2012). Decisions were taken on the material to be used in the manufacturing, quality of the materials to be used and the price of the materials. These analyses had helped the companies to make successful decisions about the materials they would use in the manufacturing of apparel (Berger, 2013). The apparels must also satisfy the customers demands and they should live up to the desired quali ty of the customers. Thus, decision tree analysis had helped the manufacturing companies to decide about the correct material of the appeals such that the price constraints, quality constraints and demand of the customers are satisfied. Australia is a large manufacturer of wool and it produces many woollen products. Manufacturing of these products increase the export of these products and provides with a huge amount of cash inflow to the companies (Wakker, 2013). The companies in turn buy different kind of raw materials that is required for the manufacturing of the apparels. These raw materials vary in their quality, price and texture. This results to cash outflow of the company. The differences between the cash inflow and the cash outflow of the company results to net present value of the company. The analysis of the net present value reveals the business of the companies of the manufacturing industries. Discussion The data required for the model of this industry includes customers demands, customers satisfaction, price of the raw material, quality of the raw material and amount of raw material required. Here, the dependent variable is customers satisfaction and the independent variables include customers satisfaction, price of raw material, quality of raw material and amount of raw material used (Larichev Moshkovich, 2013). This model gives the various environmental factors that are required for decision model of the apparel manufacturing industry. Parameters are estimated on collecting data from 10 samples. Primary data was used in this analysis. The model built for this analysis is customers satisfaction = 1.402173913 + 0.222826087* price of raw material + 0.081521739 * quality of raw material -0.016304348* amount of raw material. This is the required model for the apparel manufacturing industries in Australia (Bryce et al., 2014). It is seen that the customers satisfaction mainly depends on the price of the raw material of the apparel .This is because the price of the raw material influences the price of the apparel. The amount of raw material does not influence the satisfaction level in a positive way. Thus, this is the required model for the apparel manufacturing industry. It is usually seen that the customers mainly consider the price of the apparel as their satisfaction level. The price of the apparel mainly depends on the price of the raw materials and its quality. In this model, it is seen that the satisfaction level of the customers depend of the price of the raw materials and the quality of the raw materials, positively. Thus, the result is validated and are reliable. Conclusion It is seen that the decision model of the apparel manufacturing industry is an appropriate model as this helps the industry to decide the quality and the price of the raw materials for manufacturing. This helps the manufacturing company to take decisions in context of environmental factors and select the appropriate material regarding its quality, price and other components. Recommendation It is recommended that the apparel manufacturing companies of Australia should also consider other factors like handling of the raw materials, availability of the raw materials and the eco friendliness of these raw materials while taking decisions regarding its usage. These factors also play an important role in upholding the manufacturing industry as well as the environment. References Berger, J. O. (2013).Statistical decision theory and Bayesian analysis. Springer Science Business Media. Bryce, J. M., Flintsch, G., Hall, R. P. (2014). A multi criteria decision analysis technique for including environmental impacts in sustainable infrastructure management business practices.Transportation Research Part D: Transport and Environment,32, 435-445. Larichev, O. I., Moshkovich, H. M. (2013).Verbal decision analysis for unstructured problems(Vol. 17). Springer Science Business Media. Power, D. J., Sharda, R., Burstein, F. (2015).Decision support systems. John Wiley Sons, Ltd. Wakker, P. P. (2013).Additive representations of preferences: A new foundation of decision analysis(Vol. 4). Springer Science Business Media. Yager, R. R., Kacprzyk, J. (Eds.). (2012).The ordered weighted averaging operators: theory and applications. Springer Science Business Media.