100 words reply
This week we were asked to discuss section III of our final project and get feedback from our peers. My paper is quite long so I will break it down into a smaller explanation.
The considerations and methods of forecasting section in my project is where the different types of forecasting are discussed. It explains that analysts don’t always agree with the methods used. However, forecasting plays an important role as it provides valuable information regarding the future direction of the various factors (Doganis et al., 2008).
The data section explains the type of data and where the data was found. This is important as the analysis cannot be done without the right type of data. Since Excel is the main application to be used for the methods of analysis, it was important that the data be in a spreadsheet. It was also important for the data to separate the historical sales data by the sales dates, the prices, and the departments the items were sold from.
The methods sections explained the different types of methods that were chosen, based on the tools used and end goals of the project. Since the project was based on the three stages of empirical analysis, the methods chosen were decided on to facilitate those stages. The first method incorporated the use of pivot tables to create a user-friendly dashboard. Other methods included three types of forecasting.
The stages of empirical analysis explained each stage in-depth on how the analyses were completed. There were no problems in the first and third stages, however, the second stage where the main analysis was completed had a few issues. The analysis stage proved to be difficult as the results of the forecasting proved to be inconclusive.
The final conclusion of the project is as follows:
The data collected throughout this project was conducted to help guide a company to determine which departments will need to increase their stock to meet demand during holiday seasons. There were many key factors that this paper discussed to aid in this decision which included a literature review, the data that will be used, methods, and the three stages of empirical analysis. The literature review explained the importance of using historical data which was vital when determining the data to be used. The data chosen was decided upon because it contained several years’ worth of sales information and was in a spreadsheet, which would work well with the methods decided upon. The three stages of empirical analysis were descriptive, analytics, and theorised. They each played important roles in the assessment of the data and determining the overall goal of the project. The goal was to provide the information in a way that both analysts and company managers can understand to improve their performance and gain a greater understanding of their overall sales. The descriptive stage enabled managers and employees to visualize the data in an easy-to-understand format and select what they wanted to see at the push of a button. The analysis stage provided forecasting methods from an analytical standpoint, which is less user-friendly but equally important to understand the information at hand. Unfortunately, this stage provided inconclusive results which may be due to not having enough historical data. However, the final stage of empirical analysis provided a further review of the reasoning behind the results. The theorised stage explained some of the other factors that the dataset did not provide such as the housing market crash and what may have caused the crash in sales over this time.
With the data provided throughout this project, it is clear to see that the information can help a company understand their sales, improve their processes, reduce costs, enhance logistic timelines, and engage with customers, so long as they are prepared to put in the work to understand what has happened historically. It is equally important for them to see the data in a visually appealing way as it is for them to grasp all facets of what happened analytically and economically. While one goal was to help better plan for lack of inventory during holiday seasons, it is important to know the spending power of customers during economically hard times, such as a housing market downfall.
Doganis, P., Aggelogiannaki, E., & Sarimveis, H. (2008). A combined model predictive control and time series forecasting framework for production-inventory systems. International Journal of Production Research, 46(24), 6841–6853. https://doi.org/10.1080/00207540701523058