Pages Body Of Paperas The Company Prepares To Meet Demand

4 5 Pages Body Of Paperas The Company Prepares To Meet Demand And Ca

As the company prepares to meet demand and capacity requirements for its planned future growth, you have been asked to review the current forecasting strategy and help implement a new strategic plan for forecasting demand. The new forecasting plan ties directly to the overall strategic planning methodology established by the company. The company historically has used a time series method. The forecasting methods under consideration are the following: Qualitative: human judgment, usually best used when little data is available Simulation: the use of computer models or judgment to imitate customer behavior Causal: used when there is a direct tie between demand and an environmental factor, such as cold weather Time series: the use of historical data to predict future needs. Using course materials and other research, complete the following: Identify which forecasting technique or multiple techniques should be used in the future for the company's strategy. Are there other techniques available that are not listed above? Explain the technique you identify, and give an example of how it is used in the manufacturing, retail, and health care industries. Detail if 1 of the 4 techniques listed above should NOT be used and why. Evaluate the significance of forecasting error for the technique or techniques you have selected. What is the impact of error on your chosen technique?

Paper For Above instruction

Forecasting is a critical component of strategic planning in any business as it influences decision-making related to capacity, inventory, supply chain management, and overall operational efficiency. The selection of appropriate forecasting techniques should align with the company’s data availability, industry dynamics, and specific business objectives. Considering these factors, a combination of causal and qualitative forecasting methods would be most effective for the company’s future planning.

Recommended Forecasting Techniques for Future Strategy

Given the company’s historical reliance on the time series method, it is advisable to supplement this with causal forecasting techniques for a more comprehensive approach. Causal forecasting models, which establish relationships between demand and external environmental factors, are highly useful when market conditions or external drivers significantly influence demand patterns. For instance, if weather conditions, economic indicators, or promotional campaigns affect demand, causal models can accurately capture these relationships, allowing the company to adapt its forecasts more dynamically.

Qualitative methods, such as expert judgment and market research, should also be incorporated, especially when entering new markets or launching new products where historical data may be insufficient. Expert opinions can provide valuable insights into upcoming trends and potential disruptions, which quantitative models might overlook. For example, in the retail industry, expert judgment helps anticipate seasonal spikes or promotional impacts on demand.

Additional Techniques Not Listed

One notable technique not included in the original list is the machine learning-based forecasting approach. This technique uses algorithms that learn from data patterns over time, improving forecast accuracy by capturing complex, non-linear relationships that traditional models might miss. For example, in manufacturing, machine learning can predict equipment failures or demand surges by analyzing sensor data and historical sales. In healthcare, machine learning models can forecast patient inflow based on various factors, enabling better resource allocation.

Another technique is the ensemble forecasting method, which combines multiple forecasting models to generate a consensus prediction. This method leverages the strengths of individual models, reducing overall error and increasing robustness. Retailers often use ensemble methods to refine demand estimates during volatile periods such as holiday seasons.

Technique to Avoid: Time Series

While the company has traditionally employed time series analysis, relying solely on this method might limit the forecast's accuracy in rapidly changing environments. Time series models primarily depend on historical data, assuming that future patterns will resemble past behavior. However, in industries susceptible to sudden shifts—such as technology or retail—this assumption may lead to significant forecasting errors. Therefore, a diversified approach that incorporates other techniques like causal modeling or machine learning is recommended.

Impact of Forecasting Errors

Forecasting errors can have profound implications on business operations. Overestimating demand may lead to excess inventory, increased holding costs, and wastage, especially in perishable goods industries such as healthcare or food retail. Conversely, underestimating demand can result in stockouts, lost sales, and degraded customer satisfaction. The choice of forecasting method affects the sensitivity to errors; models that incorporate causal factors tend to adapt better to environmental changes, reducing errors during market shifts. Accurate forecasts are vital for optimal resource allocation, cost management, and strategic agility, while significant errors can compromise these aspects, leading to financial losses and diminished competitive advantage.

Conclusion

In conclusion, a blended forecasting strategy that combines causal and qualitative methods with advanced techniques such as machine learning and ensemble models will position the company for more accurate demand predictions and better strategic decision-making. While time series analysis has served as a foundation, diversifying the methodologies will mitigate risks associated with forecast errors, especially in volatile or rapidly evolving markets. Companies must continuously evaluate and refine their forecasting approaches to enhance resilience, optimize operations, and sustain growth.

References

• li. Chatfield, C. (2000). The Analysis of Time Series: An Introduction. CRC Press.
• li. Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (1998). Forecasting: Methods and Applications (3rd ed.). Wiley.
• li. Armstrong, J. S. (Ed.). (2001). Principles of Forecasting: A Handbook for Researchers and Practitioners. Springer.
• li. Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice. OTexts.
• li. Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). The Value of Machine Learning to Forecasting. International Journal of Forecasting, 34(2), 384–385.
• li. Clemen, R. T., & Reilly, T. (2014). Making Hard Decisions with DecisionTools Suite (4th ed.). Duxbury.
• li. Gardner, E. S. (1985). Exponential Smoothing: The State of the Art. Journal of Forecasting, 4(1), 1–28.
• li. Lü, L., et al. (2020). Machine Learning Techniques for Demand Forecasting in Supply Chain Management. Expert Systems with Applications, 144, 113098.
• li. Bunn, D. W. (1994). Forecasting for the Retail Industry. European Journal of Operational Research, 72(2), 245–262.
• li. Syntetos, A. A., Babai, M. Z., & Gardner, B. (2016). Forecasting in Practice: Practical examples of demand forecasting, inventory management, and supply chain planning. Long Range Planning, 49(2), 219–231.