One of the biggest challenges for business people is to evaluate customer psychology, and estimate the future demand for a product. Demand forecasting is a reliable tool to understand the environment, and utilize opportunities optimally.
Retailers need an estimate on demand fluctuations, so as to keep adequate supply of goods. Proper understanding of demand enhances competitiveness of the retailer in the marketplace. Demand forecasting is a predictive analysis technique used for understanding the future demand for products or services. The analysis and interpretation is used for forecasting consumer demand.
Understanding the demand, and accurately predicting it is vital for manufacturers, suppliers and retailers. Despite the fact, that no tool for prediction is an unmixed blessing, and has its own drawbacks, appropriate techniques can be used for forecasting, making the retailer better prepared to meet the actual demands or business opportunities as and when they arise.
Demand forecasting is generally done for the following reasons:
- Estimate rate of sale for products.
- Estimate the share of total sales range.
- Formulate demand patterns based on past performance.
- Historical analysis about the factors influencing demand for the product.
Demand forecasting brings discipline to pricing:
Crafting successful pricing strategy is a tough challenge for any business. Even as light variation in pricing causes big changes in the operating profits.Companies therefore are more focused on their pricing policies.
Implementing price policies without proper understanding of market variations can result in heavy losses for businesses. Demand forecasting helps to estimate demand and supply in individual markets. Based on these decisions, companies decide whether their prices are low or high, and formulate their pricing policies accordingly.
Demand forecasting involves both quantitative methods such as historical and current data, as well as informal methods such as educated guesses. Underestimated demand may lead to loss of sales, and overestimated demand may lead to surplus stock. Shortcoming in demand forecasting increases risks for suppliers, resulting in supply shortages, higher costs, and concerns regarding long term viability of investing in R&D. Therefore appropriate tools must be selected with care and focus, so as to come up with the right predictions. Demand forecasting is better done using multi-functional approach. Input acquired from marketing, sales, finance, and production should be analyzed and considered. Final demand forecasting is done in consensus with all participating managers.
Challenges for Demand Forecasting:
- Economic disruptions make it difficult to forecast demand.
- Volatility in the shopping nature of consumers, causes frequent shifts in shopping trends making forecasting difficult.
- Numerous promotional schemes springing up in the market place time and again make it difficult to predict demand for a specific product.
- Lack of adequate information at the supply chain, sales, or at promotional side.
- Inconsistent or unavailability of the required data from channels make forecasting process difficult.
Apparel retailers adopt multi channel marketing initiatives to promote their merchandise. Therefore, they face more challenges than any other vertical to absorb the strategy. Apparel sales are influenced by external factors such as price changes, color, climate etc, and internal factors like time. Trends of the apparel sector are volatile in nature and remain unpredictable. Retailers need successful inventory management so as to grasp the opportunities as they emerge.
Demand forecasting makes it easy for a retailer to go for optimized revenue, and replacement of goods and products. It is an integral part of the supply chain activities, as it is required to sustain profitability. Accurate forecasting ensures optimal inventory management. New approaches in forecasting demand will ensure accuracy of business plans, satisfaction of customers, and contribute of overall efficiency, and reputation of the retail business.
References:
1) statsoft.com
2) cgdev.org
3) smetoolkit.org
4) Mckinseyquarterly.com
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