Forecasting reply (2 replies of 150 words each)
39059Each reply must incorporate at least 2 scholarly citation(s) in APA format. (each reply should be 150 words)
Reply to Hannahn…
Walmart Forecasting
Forecasting is the decision making tool an organization utilize to help in budgeting, planning, organizing, and estimating the future growth of the company. It helps companies plan in ahead and predict the future outcomes of the company based on previous events and management rights. A lot of companies practice this method and have succeeded in it. Surprisingly, Walmart which if known around the country as the biggest retailer, uses forecasting to determine future demand. According to an article, “Accurate forecasting is extremely important to most businesses and Walmart is no exception. Forecasts are leveraged throughout the company in domains as varied as finance, real estate, supply chain, marketing, and human resources”.
Walmart as the biggest sales organization uses forecasting as using a range of techniques, such as analyzing the previous sales data to examine the future ones, market trend, and macroeconomic inventory and other operational choices. They see it as “The accuracy of the forecast value will influence the formulation of future policy”. Walmart at this point, is significantly impacted by outlook. Sales forecasting is a crucial tool for a business like Walmart. This is why the company employs a range of techniques. Analyzing previous sales data and macroeconomic factors are all part of it. Decisions about inventory, employment, and other operational matters are based on Walmart’s sales estimate.
Word Citation
Cui, X., Shaojun, E., Niu, D., Wang, D., & Li, M. (2021). An Improved Forecasting Method and Application of China’s Energy Consumption under the Carbon Peak Target. Sustainability, 13(15), 8670. https://doi.org/10.3390/su13158670
Seaman, B., & Bowman, J. (2022). Applicability of the M5 to forecasting at walmart. International Journal of Forecasting, 38(4), 1468-1472. https://doi.org/10.1016/j.ijforecast.2021.06.002 Links to an external site.
Fan, Z., Che, Y., & Chen, Z. (2017). Product sales forecasting using online reviews and historical sales data: A method combining the bass model and sentiment analysis. Journal of Business Research, 74, 90-100. https://doi.org/10.1016/j.jbusres.2017.01.010 Links to an external site.
Reply to Dylan…
Discussion 1: Forecasting (Option A)
Forecasting plays a critical role in operations management, as understanding the anticipated quantity and timing of demand is essential for successful supply chain management (Stevenson, 2021). In fact, since matching supply to demand is one of the fundamental objectives of operations management, “the importance of forecasting to operations management cannot be overstated” (Stevenson, 2021, p. 76). Due to the immense value it can offer firms, it is no surprise that organizations utilize forecasting to gain insight into future demand conditions. One recent example is Nestle which has implemented demand-driven forecasting solutions in order to update and improve their forecasting capabilities (Pierce, 2020).
The forecasting technology used at Nestle evaluates demand signals as opposed to trends and seasonality in order to determine what factors actually influence consumer purchase volume (Pierce, 2020). Essentially, “demand-driven forecasting aims to sense demand signals and shape the future customer demands by utilizing advanced data mining techniques” (Kumar et al., 2020, p. 494). This forecasting at Nestle also minimizes human judgement, allowing analytics technology to handle most of the forecasting, thereby increasing accuracy by removing the potential of personal bias that can undermine precision (Pierce, 2020). This has allowed Nestle to be proactive rather than reactive, enabling demand shaping for their products (Pierce, 2020). Leveraging the ability to forecast based on what-if scenarios, Nestle was able to determine how well certain promotions drive incremental demand, and even revenue, enabling them to identify effective and profitable promotions that can be used to influence demand (Pierce, 2020). Ultimately, this forecasting technology has improved the accuracy of Nestle’s forecasts, enabling the company to reduce inventory by millions of dollars, and still meet customer demand (Pierce, 2020).
Overall, the application of forecasting at Nestle is just one example among many of an organization utilizing such techniques to gather information about future demand. After all, forecasting demand forms the foundation for planning all operational activities (Kumar et al., 2020). In our data-driven world, analytical and technological approaches are offering increasingly advanced capabilities in countless areas, and forecasting is no exception. Demand-driven forecasting is supported by machine learning and big data technologies, leveraging advanced data analytics technologies to improve upon traditional methods of forecasting demand (Garinian et al., 2023). This enhanced accuracy of demand forecasting enables organizations to reduce uncertainty and better control inventory levels to minimize costs (Garinian et al., 2023).
References
Garinian, E. O. M., Fierro, T. E. S., Saucedo, J. A. M., & Aguilar, R. R. (2023). Machine Learning Applications for Demand Driven in Supply Chain: Literature Review. Engineering Cyber-Physical Systems and Critical Infrastructures, 1, 763–772. https://doi.org/10.1007/978-3-031-09753-9_59
Kumar, A., Shankar, R., & Aljohani, N. R. (2020). A big data driven framework for demand-driven forecasting with effects of marketing-mix variables. Industrial Marketing Management, 90, 493–507. https://doi.org/10.1016/j.indmarman.2019.05.003
Pierce, F. (2020). How Demand-Driven Forecasting paid off for Nestle. Supply Chain Digital. https://supplychaindigital.com/logistics/how-demand-driven-forecasting-paid-nestle
Stevenson, W. J. (2021). Operations Management (14th ed.). McGraw-Hill Education.
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