Record & upload to bCourses a (maximum) 5 minute pitch, using a maximum of 5 slides.

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ENGIN295

Assignment#3 Op Ed

Hanzhi Wang (Abby)

‘Forecasting Demand of a New Product’ Science Opinion Editorial

Have you thought about that your one-sentence review on online marketplace will influence the launch of new product and bring millions profit to the website by contributing the data to demand prediction? Sound predictions are vital in managing since executives have to forecast every time, they need to make a decision to adapt to the dynamic demand, competition, strikes and economic fluctuations (Tehrani & Ahrens, 2016). The more managers use this prediction technique, the better they get prepared to handle any situation that the unpredictable nature of business presents. This project explains the capability of forecasting techniques with the main emphasis on Palo Alto products, and how new products can reach their full potential using forecasting techniques. To deal with the increasing variety and intricacy of administrative issues, many forecasting tactics have been created recently, each with its specific use (Schneider & Gupta, 2016), resulting in a need to create a formula that helps in choosing the best forecasting technique.

Specifically, this is a proposal of using data and challenges presented by a shopping website, www.JD.com which is the one of the biggest B2C online retailers in China, to accurately predict the trends of a new product. The entry points, success strategies, and challenges can be used to determine the anticipated challenges while the marketing team, on the other hand, can assimilate the reference product’s strategies. The success of this process can be measured by assessing the profit margins, and the online popularity index on platforms e.g. Google, consumer reviews, Twitter, Facebook and Instagram (Lei et al., 2019). After an accurate prediction, products are more likely to achieve more sales that when placed into the market blindly, resulting in more profit for the managers.

By using data to help managers become experts in selecting the most appropriate technique, this project will create a small but essential step towards revolutionizing the process of product penetration. It will provide a multidisciplinary and long-term solution to figuring out product predicted good-fortunes or flops

References

Lei, M., Li, S., & Yu, S. (2019). Demand Forecasting Approaches Based on Associated Relationships for Multiple Products. Entropy, 21(10), 974.

Schneider, M. J., & Gupta, S. (2016). Forecasting sales of new and existing products using consumer reviews: A random projections approach. International Journal of Forecasting, 32(2), 243-256.

Tehrani, A. F., & Ahrens, D. (2016). Improved forecasting and purchasing of fashion products based on the use of big data techniques. In Supply management research (pp. 293-312). Springer Gabler, Wiesbaden.