I need a reply to the following two posts below. 250 words EACH with a biblical integration.
Company forecasting is essentially a prediction based on the companies past and they will utilize that to make decisions about the future. However, the traditional understanding of that metric was thrown out the window in 2019 when the COVID-19 pandemic had begun. One of the major effects we are seeing in present-day to show just how powerful the old school projections and mindset of forecasting really is, is the current supply chain crisis regarding demand. It is far more important than ever to keep a record of accurate information and data for a company to survive, “Companies are more global today than ever before; lead times have been extended with the effective practise of lean management made difficult by the volatility of demand. The use of safety stock or inventory to protect against variability is no longer so viable and, as Chase asserts, companies now need to understand and measure that variability in demand and be able to predict it more accurately with an enterprise-wide solution, which can look at millions of forecasts up and down a product hierarchy,” (Pierce, 2020). Nestle is a perfect example of this as Charles Chase who is an expert in sales forecasting stated, ” Nestl improve its forecast accuracy and make multi-million dollar reductions in their inventory by removing human judgment and enabling the predicting of future demand through demand shaping,” (Pierce, 2020). Chase goes on to state that nearly 80% of Nestles forecasts are driven right out of the solution with no human judgment at all, and only 20% require any kind of human judgment,” (Pierce, 2020).
Why is this important? It is proof that sometimes a more qualitative approach can be just as successful as a quantitative by removing human error and discrepancies, ” The quantitative forecasting method relies on historical data to predict future needs and trends. The data can be from your own company, market activity, or both. It focuses on cold, hard numbers that can show clear courses of change and action. This method is beneficial for companies that have an extensive amount of data at their disposal,” (Vazquez, 2021). I personally think the best option would be to use a combination of both qualitative and quantitative forecasting to meet supply and demand. However in the case of Nestle, they proved that their strategy worked well for their business model.
Article – https://supplychaindigital.com/ logistics-1/how-demand-driven-forecasting-paid-nestle
Citations
Pierce, F. (2020). How Demand-Drive Forecasting paid off for Nestle. Retrieved 16 January 2022, from https://supplychaindigital.com/logistics-1/how-demand-driven-forecasting-paid-nestle
Vazquez, A. (2021). What Is Business Forecasting? Predictions to Drive Success. Retrieved 16 January 2022, from https://learn.g2.com/business-forecasting
Forecasting is essential for business to meet supply demand as it is the primary goal of operations management. Businesses make plans for future operations based on anticipated future demand (Stevenson, 2021, pg. 77). It serves as a basis for a large portion of company decisions from budgeting to personnel. Poor forecasts can result in stock-outs or overstock situations, which have a direct impact on the companys profitability and may also decrease customer satisfaction and market share (van Steenbergen & Mes, 2020, para. 1). No business wants to come up short for demand as it could prevent them from losing potential profits. Accurate demand forecasting for products is particularly critical in several industries that are characterized by strong time-based competition (Belvedere & Goodwin, 2017, para. 1). This only causes more tension as it could be sending customers to competition who had better forecasted future product demand.
One company, Centricity Inc., is a new startup based in New York City. They use artificial intelligence to help companies in their demand forecasting methods. Their AI algorithms analyzes 2.5 billion data points worth of internet traffic a day to predict demand for products so that retailers can stock their shelves accordingly (Castellanos, 2021, para. 2). A lot of the data is based on users location, the websites they visit, specific terms used, as well as pages they click on. After compiling the data, they are able to obtain and analyze where groups of people are buying a certain product within a given area. Their customers, who are other companies, use their findings to predicts what customers will be wanting to buy within the range of one to three months. Those companies can then begin their process of buying the right inventory as well as a more accurate amount. While these numbers are not 100% accurate, they do provide a better footing for organizations.
References:
Belvedere, V., & Goodwin, P. (2017). The influence of product involvement and emotion on short-term product demand forecasting. International Journal of Forecasting, 33(3), 652-661. https://doi.org/10.1016/j.ijforecast.2017.02.004
Castellanos, S. (2021, January 13). Ai startup sees opportunity forecasting pandemic-era consumer demand. The Wall Street Journal. Retrieved January 17, 2022, from https://www.wsj.com/articles/ai-startup-sees-opportunity-forecasting-pandemic-era-consumer-demand-11610496808
Stevenson, W. J. (2021). Operations management (14th ed.). McGrawHill.
van Steenbergen, R. M., & Mes, M. R. K. (2020). Forecasting demand profiles of new products. Decision Support Systems, 139, 113401. https://doi.org/10.1016/j.dss.2020.113401
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