Research On Supply Chain Forecasting Methods
Maintaining the exact volumes of products in stock is essential for every business. To forecast supply, managers use data from their previous supplies to gain insights and understand the demand. Supply forecasting enables the managers to formulate informed decisions for their business as per as stock inventory, cargo booking, or budget planning are concerned (Makridakis et el., 2018). Forecasting in the supply chain enables managers to know when they are supposed to order products and how they should be, whether raw materials, semi-manufactured, or entirely manufactured products. Forecasting also involves analyzing demand to know how willing your customers are to purchase your firm’s products. This paper aims to give an overview of supply chain forecasting methods. Supply chain forecasting is essential to professional managers and other people like me because it helps them make orders on time, avoid unnecessary inventory expenses, and plan for price changes (Makridakis et el., 2018).
Overview of forecasting methods
There are two main methods of forecasting in the supply chain; quantitative and qualitative forecasting methods. The quantitative forecasting methods depend on historical data to estimate future sales. At the same time, the qualitative relies on the insights, expertise, and experience of those involved in the firm (Makridakis et el., 2018).
Straight-line method:
The straight-line method is a simple forecasting method that uses historical information and trends to estimate the future revenue growth rate. This method is mainly used when a firm is experiencing a growth period and at the same time expecting an increase in sales (Arvan et el., 2019). The straight-line method is computed by looking for the past revenue growth and applying it to future operations. The method considers the extended period of the business, beginning from when the business was established up to the time the method is being calculated. The straight-line forecasting method assumes that the sales of a firm will continue to increase. However, the straight-line method has limitations because it does not consider some variables such as seasonality and potential short-ten economic factors (Arvan et el., 2019).
Moving average method
The moving average is a forecasting method that looks at underlying data sequences to estimate future values (Arvan et el., 2019). The moving average method and the straight-line method are almost the same only the moving average looks at sections instead of the entire set of the historical data. The moving average method only accounts for a particular set of information perceived to be of great importance to the firm. Furthermore, the moving average method puts into consideration only the period that is required brackets. For this method to make sense and be understood easily, it must be plotted on a graph to see the trends (Arvan et el., 2019).
Multiplier linear aggression
The linear multiplier aggression is a forecasting method used to estimate the outcome of one variable based on the values of multiple variables. It is an extension of simple linear regression that compares several independent variables to predict the expected value (Contreras et el., 2020). The multiplier linear aggression is among the most reliable forecasting methods since it considers not just a single variable but several independent factors. This method gives more accurate predictions about consumer behavior as well as future marketing expenditure.
Linear aggression area
The linear aggression area estimates the value of a dependent variable based on the value of the independent variable. The linear aggression area method predicts the coefficient of a linear equation with one independent variable to forecast the value of the independent variable. It compares one independent variable with one independent variable to establish the relationship between them (Contreras et el., 2020).
Importance of supply chain forecasting methods to professional managers
Supply forecasting greatly benefits professional managers since it provides the ground for them to make informed decisions and formulate data-driven business strategies (Contreras et el., 2020). Financial and operational decisions if any business is done based on the current situation of a firm and the forecast on how the future might be. Forecasting makes professional managers proactive to the changes in the business environment. Forecasting enables professionals to make a reasonable and attainable set of goals based on the past and the current information (Contreras et el., 2020). forecasting also helps professionals estimate the budget to be allocated to specific products and the time needed for the offerings. Furthermore, professionals benefit from forecasting since estimating what could happen in the future enables them to make the appropriate strategy adjustments to better operations and achieve better outcomes (Contreras et el., 2020).
Proper supply forecasting similarly helps professionals ensure that adequate supply is maintained to satisfy the public demand fully. Demand forecasting provides business managers with crucial information about their business potential in the current markets. Proper forecasting enables professionals to optimize inventory, increase inventory turnover rates, and minimize holding costs in their businesses (Contreras et el., 2020).
Conclusion
Forecasting plays a crucial role in making business decisions and formulating sustainable business strategies. Historical data on the supply patterns provides the basis for future forecasting, and it has to be combined with insights and knowledge on current demand. Knowing how to forecast supply chain demands correctly is vital for ensuring a firm’s success. Forecasting methods such as moving average, straight-line, multiple linear regression, and simple linear regression can be used by any professional to forecast the future values for any dependent variable. Supply forecasting greatly benefits professional managers since it provides the ground for them to make informed decisions and formulate data-driven business strategies. Both financial and operational decisions if any business is done based on the current situation of a firm as well as the forecast on how the future might be
References
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and Machine Learning forecasting methods: Concerns and ways forward. PloS one, 13(3), e0194889.
Arvan, M., Fahimnia, B., Reisi, M., & Siemsen, E. (2019). Integrating human judgment into quantitative forecasting methods: A review. Omega, 86, 237-252.
Contreras-Choccata, D., Sotelo-Raffo, J., Raymundo-Ibañez, C., & Rivera, L. (2020, February). Demand Management Model Based on Quantitative Forecasting Methods and Continuous Improvement to Increase Production Planning Efficiencies of SMEs Bakeries. In International Conference on Intelligent Human Systems Integration (pp. 760-765). Springer, Cham.
Contreras, G. S., Mora, M. R., & Gómez, P. J. (2020). Use of Quantitative Forecasting Methods and Error Calculation for Better Adaptability to the Application of a Mathematical Model to Determine the Speed of Spread of a Coronavirus Infection (COVID-19) in Spain.