Quantity Recommendations for the Hong Kong Factory
The “Sample Buying Committee Forecasts” table presents the managers’ suggestions regarding the expected demand. One should immediately highlight that “the forecast is always wrong” (Simchi-Levi et al., 2021, p. 39). However, it is possible to increase its effectiveness by relying on aggregate data and utilizing the available data. Since the Hong Kong factory is under analysis, one should explain that its facility features high labor costs, increased quality, and shorter performance rates (Simchi-Levi et al., 2021). These conditions denote that this location is appropriate to produce a significant part of ski wear. Considering the fact that the forecasts represent a normal distribution, it is possible to rely on a standard deviation (van Steenbergen & Mes, 2020). According to this approach, it is rational to increase the average forecast by one standard deviation. Thus, the production quantity recommendations are as follows: 1,211 (Gail), 1,365 (Isis), 1,606 (Entice), 2,865 (Assault), 1,481 (Tetri), 2,554 (Electra), 1,637 (Stephanie), 4,573 (Seduced), 4,343 (Anita), and 3,080 (Daphne). These suggestions result in 24,715 women’s parkas that will be ordered from the Hong Kong factory.
Measuring Risk
There is no doubt that the ordering policy above implies some risk, and it is necessary to measure it. A suitable method to quantify risk is to rely on the coefficient of variation. Also known as risk pooling, this indicator can be found by dividing the standard deviation by an appropriate average value (Simchi-Levi et al., 2021). This value is helpful because it reveals how much volatility is present in an individual forecast (Abolghasemi et al., 2020). Thus, the coefficients are presented below: 0.19 (Gail), 0.31 (Isis), 0.18 (Entice), 0.13 (Assault), 0.35 (Tetri), 0.19 (Electra), 0.47 (Stephanie), 0.14 (Seduced), 0.32 (Anita), and 0.29 (Daphne). These findings demonstrate that some styles offer less volatility while others bring riskier forecasts. An effective approach to mitigate risk is to rely on aggregate predictions since these data typically imply less volatility. The rationale behind this statement is that high demand for one style can be balanced by low demand for a different one (Simchi-Levi et al., 2021). That is why it is reasonable to rely on risk pooling to manage risk.
Quantity Recommendations for the Chinese Factory
A similar methodology can be used to develop quantity recommendations for the Chinese factory. This time, it is necessary to subtract a standard deviation from the average value. This recommendation is present because productivity was lower in this location, and the goods were of lower quality (Simchi-Levi et al., 2021). Shen and Chen (2020) mention that additional quality management efforts are needed when production is outsourced. Furthermore, the US government significantly limited the number of units that could be imported from China (Simchi-Levi et al., 2021). Finally, it is rational to remember that “the minimum production quantity for a style was 1,200 units in China” (Simchi-Levi et al., 2021, p. 469). That is why it is impossible to order below the stipulated figure. Consequently, there are following quantity recommendations: 1,200 (Gail), 1,200 (Isis), 1,200 (Entice), 2,185 (Assault), 1,200 (Tetri), 1,746 (Electra), 1,200 (Stephanie), 3,461 (Seduced), 2,249 (Anita), and 1,686 (Daphne). These figures result in a total of 17,327 units that should be manufactured at the Chinese factory. Thus, the main difference refers to the fact that reduced quantities should be obtained from this facility.
Operational Changes
It is possible to offer a few operational changes that Wally could use to improve performance. Firstly, he could benefit from relying on big data analytics to forecast demand. According to Seyedan and Mafakheri (2020), this approach deals with demand prediction, trend analysis, and customer behavior analysis. This innovative decision could bring more clarity and accuracy to make more accurate forecasts. Secondly, Wally could implement a different approach regarding how the final predictions were made. After Wally questioned all the managers independently, it could be rational to organize a group meeting to discuss their suggestions. This step is beneficial because it would demonstrate how the managers defended their opinions against their colleagues’ criticism. This productive discussion could demonstrate which suggestions were the most reasonable. Finally, the involvement of all managers in this activity could allow them to experience dignity since they participated in making significant decisions (Keller & Alsdorf, 2012). It is possible to expect that these operational changes could lead to essential advantages for the entire organization.
Sourcing Policy
The current sourcing policy should evoke particular thoughts for Wally. In the short term, this approach is acceptable because it allows the business to produce the required number of units and satisfy the market demand. One should admit that outsourcing is a suitable way to manufacture goods at reasonable prices. However, the long-term approach to the existing state of affairs implies that some changes are needed. In particular, Wally should consider finding a new factory instead of a Chinese one. The rationale behind this statement is that the US government implies some tariffs on products from China and that the given factory features low-quality goods. A suitable intervention is to look at Indonesia because this country offers profitable legal regulations and a relatively skillful workforce (Farida et al., 2020). This sourcing policy could help Wally improve the quality of goods and minimize risk.
References
Abolghasemi, M., Beh, E., Tarr, G., & Gerlach, R. (2020). Demand forecasting in supply chain: The impact of demand volatility in the presence of promotion. Computers & Industrial Engineering, 142, 106380. Web.
Farida, I., Setiawan, R., Maryatmi, A., Juwita, M., & Muqsith, M. (2020). Outsourcing policy in Indonesia. American Research Journal of Humanities & Social Science, 3(10), 26-31.
Keller, T., & Alsdorf, K. (2012). Every good endeavor: Connecting your work to God’s work. Penguin Random House.
Seyedan, M., & Mafakheri, F. (2020). Predictive big data analytics for supply chain demand forecasting: Methods, applications, and research opportunities. Journal of Big Data, 7(1), 1-22. Web.
Shen, B., & Chen, C. (2020). Quality management in outsourced global fashion supply chains: An exploratory case study. Production Planning & Control, 31(9), 757-769. Web.
Simchi-Levi, D., Kaminsky, P., & Simchi-Levi, E. (2021). Designing and managing the supply chain: Concepts, strategies and case studies (4th ed.). McGraw-Hill Education.
Van Steenbergen, R. M., & Mes, M. R. (2020). Forecasting demand profiles of new products. Decision Support Systems, 139, 113401. Web.