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When it comes to businesses, many might struggle with forecasting, planning, producing, and distributing short life-cycle products due to inadequate calculations, lack of information, or inefficient approaches. For example, one of the problems connected with forecasting short life-cycle products like fashion apparel or electronic devices is a lack of past sales data and a solid sense of how rapidly the item will become popular (Sainathuni et al., 2019). Additionally, the product release alone might distort early estimates of demand, making forecasts less accurate and making inventory management more difficult.
However, forecasting is not the only field that has problems. Planning can also be seen as a problematic sphere due to the lack of data on product performance and predictions. In turn, distribution planning becomes more complicated when facilities manage goods with both extended and short life cycles (Sainathuni et al., 2019). Every item class has diverse market demands and life cycles since fresh foods will have a shorter life span than fashion apparel. This necessitates different strategic choices with potentially divergent goals—cost-efficiency for basic products and time responsiveness for apparel items (Sainathuni et al., 2019). However, most businesses continue to operate as though the world is predictable. To allow enough time for effective manufacturing and distribution, they base their production planning on demand predictions generated far before the selling period. It is also frequently regarded as an issue when that technique leads to scarcity of some products and streams full of out-of-date components and finished goods because projected hot sellers have collapsed (Fisher et al., 1994). The truth is, however, that most businesses struggle to adequately account for demand volatility in their production-planning procedures.
The aforementioned factors can be viewed from the perspective of Sport Obermeyer. The company improved its forecasting by using demand variables more creatively and establishing a method to monitor predicting mistakes (Fisher et al., 1994). Sport Obermeyer redesigned its planning processes by creating an ideal manufacturing schedule and developing a sophisticated computerized mathematical model (Fisher et al., 1994). The framework depicts the items that should be manufactured in a nonreactive manner as well as the best levels of production (Fisher et al., 1994). Thus, forecasting, planning, producing, and distributing short life-cycle products should be based on efficient models and responses.
References
Ahmad, M.T. & Mondal, S. (2019). Dynamic supplier selection approach for mining equipment company. Journal of Modelling in Management, 14(1), 77-105.
Fisher, M. L., Hammond, J. H., Obermeyer, W. R., & Raman, A. (1994). Making supply meet demand in an uncertain world. Harvard Business Review, 72, 83-83. Web.
Pardede, S. R., & Vanany, I. (2021). Analysis and Control for Heavy Equipment Spare Parts Inventory in the Nickel Mining Industry. IPTEK Journal of Proceedings Series, 6, 478-483.
Sainathuni, B., Guthrie, B., Parikh, P. J., & Kong, N. (2019). Distribution planning for products with varying life cycles. Flexible Services and Manufacturing Journal, 31(1), 41-74.
Teixeira, C., Lopes, I., & Figueiredo, M. (2017). Multi-criteria classification for spare parts management: A case study. Procedia Manufacturing, 11, 1560-1567.
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