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Forecasting is important in modern supply chain management, especially in companies that manufacture items on inventory rather than by order. To ensure that they produce the right level of materials that satisfies their customers, manufacturers rely on material forecasting.
This enables them to avoid producing an overcapacity of goods that will store in the market. Additionally, a manufacturer is to fulfill his customer’s demand and thus be able to forecast it to avoid financial catastrophe.
A forecast should be reviewed regularly by the management in order not to be static. The reason for this is to enable the inclusion of information on the future trends, external or internal environment to give a more accurate forecast.
Having an accurate forecast is very important task in supply chain management. Since company depends on the investment in the inventory, forecast accuracy is very important to the bottom line. Safety –stock levels needed to reach targets can be reduced if accuracy across all range of SKUs can be improved.
For proper allocation of supply chain resources, forecast accuracy is central at primitive SKU level. A supply imbalance can be met if there is an inaccurate demand forecast (Burt et al, 2010).
The importance of forecasting within the form cannot be overstated; managers use forecast generation and sharing to guide the distribution of resources, provide target for organizational efforts, sales, and product development and integrate the operation’s management function with marketing (Burt et al, 2010).
The main output required from forecasting is the determination of how many products will be bought by customers. Acquisition of too much raw materials may result from an overly optimistic forecast while a too low forecast may not match customer’s demands and lead to lose of customers (Burt et al, 2010).
An anticipated demand profile is typically delivered over a number of months for the top level products. This will in turn drive procurement activities that utilize known lead time to enable the procurement of materials to meet the forecast requirements.
Actual forecast varies from company to company; however, the key concepts are utilized commonly in most businesses.
Many companies utilize forecasting software that tackles data from the ERP and extrapolates it into demand profile for the future.This data analysis method is supported by market intelligence to tune the demand profile into what is thought to be realistic (Chockalingam, 2009).
This approach follows data integrity, and thus it is very beneficial to the organization. The company bases its analysis on the non subjective transaction data as the forecast is configured in a way that its solution delivers a number of outputs depending on pre-configured criteria.
The measure of how close actual demand is to the forecast quantity is referred to as the forecast accuracy. The converse of error gives the forecast accuracy.
Accuracy (%) =1 – Error (%).
Non Statistical forecast
This forecast is determined by production plainness. It is defined from quantities which are based on the current demand (Chockalingam ,2009).
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Forecasting is based on complex calculation; historical periods are used to determine future demands. This gives a planner a guide to future demand. It is impossible to attain accurate forecast. Therefore, planners’ knowledge of the future is important in determining the products future demand. The usefulness of an accurate forecast is that it enables the supplier to plan its strategy tactfully.
In addition, it allows for the limited flexibility to reschedule resources. Hence, the inability to maintain an accurate forecast can result in decrease of sale, customers, excess inventory which may result in loss of goods and other inefficiencies.
Other than that, the benefit of maintaining an accurate forecast is that it helps in capacity planning and setting strategic initiatives. This approach allows for the flexibility to change and err (Chockalingam ,2009).
Burt, D. N., Petcavage, S. D., & Pinkerton, R. L. (2010). Supply management (8th ed.). Boston: McGraw‐Hill.
Chockalingam, M.N. (2009). Forecasting accuracy and safety stock strategies. [PDF document]. Retrieved from Demand Planning LLC: https://demandplanning.net/documents/dmdaccuracywebVersions.pdf