This unit 4 discussion assignment will examine and explain how the use of supply chain analysis and forecasting in today’s industry. Forecasting and analysis represent a sketchy or flawed science, however, it is deemed as a necessity for most companies or businesses. This is especially true when pertaining to supply chain management. Using appropriate analysis and forecasting methods can help to ensure that there is enough supply to satisfy both need and demand. Supply chain management systems are used by business analyst along with other various forecasting tools and methods in order to measure the status for both need and demand in advance by weeks and months (In Stadtler, In Kilger, & In Meyr, 2015). A company or a business must not only be on point, they must be focused as well when trying to meet the needs and demands of their consumers/customers. Any aberration or inaccuracy of a demand will more than likely lead to and cause high costs along with a bloated inventory. In some instances when a consumer/customer does not receive the product or merchandise ordered, this may be the result demand for that product or merchandise being underestimated or miscalculated. Supply chain management consist of methods and procedures that ensure the ability of a company or business the means to meeting a demand by having enough supply.According to (APICS), the Association for Operations Management, supply chain management is comprised of the following factors; design, planning, implementation, monitoring and control of the activities surrounding the supply chain process or scheme (Buckley, Chan, Chen, Tien, & L.-C, 2001).
Trying to forecast demand while also coordinating the activities of supply chain is viewed in many ways as a full-time job. A company or a business with domestic and global operations uses practical systems and software to assist with forecasting demand. Forecasting involves the process or method of gathering information and data in order to predict possible trends based on past and present analysis. The uncertainty surrounding any possible risk are important to forecasting and prediction (Sethi, Yan, & Zhang, 2005). The forecasting method has various types of methods which may prove to be significant for any company or business within the supply chain scheme or process. They are as follows;
Qualitative vs. quantitative
Seasonal naïve approach
These are just some of the forecasting methods and procedures used by a company or business within the supply chain system that help ensure planning, budgeting and future growth. The overall objective or goal of forecasting is to produce or develop improved performance of a company or business by increasing profit, revenue and more importantly consumer/customer satisfaction.
Forecasting methods can be analyzed under two main categories; qualitative and quantitative. Qualitative forecasting is generally used when there is no historical data is available. Qualitative forecasting is based on subjective information and is generally used for long-term forecasting. Some common qualitative forecasting methods are; expert opinion, focus groups and market research. On the other hand, quantitative forecasting uses historical demand data to project future demand with intrinsic and extrinsic factors. Intrinsic factors are the factors that are internal and under control of the company. Extrinsic factors are external and outside of the control of the company. Most common quantitative forecasting method is perhaps the time-series forecasting model. According to Zhang(2001), some of the different approaches to time series forecasting are; moving average, exponential smoothing, and autoregressive integrated moving average (ARIMA) . These methods are all linear, and simplistic in understanding and implementation. Zhang(2001) states that, some of the nonlinear models are, the bilinear model, autoregressive (TAR) model, and the autoregressive conditional heteroscedastic model (ARCH). In the recent years, in addition to these models, artificial neural networks have been suggested as an alternative to time series forecasting method. According to the research conducted by Ciucu and Oancea (2013), time series forecasting financial data using neural networks have proven to perform better than the classical method. Decomposition forecasting method is popular among forecasters because it is easier to understand and explain as opposed to ARIMA models. According to Metcalf (2017), decomposition forecasting method, separates or decomposes historical data into different components and uses them to create a forecast that is more accurate than a simple trend line. Furthermore, with the decomposition method, you can assess the importance of each component and emphasize them according to the changing intrinsic or extrinsic factors. Another important and commonly used method is called exponential smoothing. Exponential smoothing has many variations. According to Arsham (2015), Exponential smoothing simply assigns exponentially decreasing weights to the data as it gets older. So, the recent data is given more weight in the forecast. I believe exponential smoothing is a great and simple method for companies that are new to forecasting and want to see the accuracy of these methods. Another method that can be used by the Stone Horse Supply Company is the decomposition method. It provides accurate forecasts while having the ability to adapt to changing industry conditions and even seasonality.
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