What is demand forecasting?
Organizations are likely to face several internal and external risks including high competition, failure of technology, labor unrest, inflation, recession, and change in government laws. The adverse effects of risk can be reduced by determining the future demand or sales prospects of their offerings. Demand forecasting is a technique used to estimate the probable demand in the future for a product or service. It can also be defined as a systematic process that involves anticipating the future demand for the product and services offered by a company under a set of uncontrollable and competitive forces.
Steps in demand forecasting
Prepare the data
The accuracy of forecasting largely depends on the data collected. Managers usually gather specific transactions that they must at a higher level to get a picture of meaningful sales activities and trends. In this process, they are required to create a number of dimensions for study. With the help of a data warehouse or database can support multiple types of aggregations and enable flexible analysis across dimensions instantly. In the case of demand forecasting, a higher level of aggregation means more accuracy in the forecast.
Measure data accuracy and coverage
Analyzing sales history is one of the most common and best methods that aid in demand forecasting. There are dozens of methods at disposal to analyze sales history, this includes the simple moving averages to advanced regression methods. They can be used to measure trends, seasonality, and cyclic characteristics of the company’s data. Prior to deciding on the ideal method, managers must establish whether the sales history is the same as demand for their product and services. They must also analyze stock out situations and accommodate this while predicting future demand.
Most businesses encounter stock out situations. However, in several cases, the demand is fulfilled through alternate channels such as an expedited order from a different geographic location. Though this results in customer delight, in the bigger picture this creates a chaos in the company’s demand forecasting efforts. This is because, during data collection, such exceptional cases cannot be easily tracked. Consider another situation where customer opts for a substitute product as their preferred product is out of stock. This distorts the demand estimate, potentially driving down inventory for the stockout product and driving up inventory for a substitute product. Though stock-outs can result in glitches in the demand forecasting process, this can be resolved to a large extent by correctly recording the place, time and item where the transaction actually occurred, along with availability.
Manage spikes in data
Occasional spikes often occur in the case of most businesses. While in some cases they reflect the real sales sometimes it could also be data errors. These spikes tend to pull the demand distribution in their direction, consequently skewing the inventory planning. To stay out of such situations, spikes should be researched separately in order to better understand what caused them and whether they are recurring or one-time events. It is ideally advised to avoid eliminate the spikes from demand forecasting estimates and replace the data points with a more typical observation such as the average volume for the previous and subsequent time periods.
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