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Imagine if you knew what products & services customers wanted and when they wanted them. Such knowledge could transform the way your organization does business. Now, with demand forecasting, attaining that knowledge is possible. Demand forecasting is the use of complex algorithms acting on datasets to forecast product or service demand.Essentially, it’s a mathematical peek into the future. Of course no prediction is perfect, so confidence thresholds would play a part in helping you to determine the viability of forecasted consumer behavior.
Demand forecasting enables your organization to make significant changes to how you market, price, and plan the sale of your products. For example, historical (time-series) or regression analysis (causal) methods could be used to forecast expected sales of a type of shoe during a specific time period. Such a forecast, when combined with an analysis of consumer, cost, and competition, would enable you to make accurate pricing decisions. Add-in logistical and storage data, and you could assess future capacity requirements, ensure availability, and adapt vendor/distribution logistics to realize customer satisfaction.
The efficacy of predictive analytics is highly dependent on the accuracy of the source data being used. This is because forecasts typically rely on numerous datastores and multiple factors, particularly when confidence thresholds are calculated. Given this, it’s critical that the data used is error-free (see "Forecasting Time Series with R"); of course, 100% clean source data is not a realistic expectation.
Data Science Studio (DSS) is a powerful predictive analytics solution that places a high value on the data management process. We realize that preparing data is a time-intensive and laborious process that accounts for the majority of time spent by data scientists. DSS addresses this issue by automatically discovering and conveying wrong data (e.g., outliers, blank fields, incorrectly formatted, etc.), and then suggesting relevant transformations. Over 65 integrated click-and-go processors are also available to facilitate the data cleansing and enrichment process. Data preprocessing scripts can be saved as recipes and automatically run in order to format and clean data so that it meets your data forecast requirements.
Post-cleansing, DSS can then be used to build intelligent demand forecast models. These models can use advanced analysis methods to gain a greater understanding of how your customers are expected to engage with your product or service. Forecasted results help to comprehend the contrast between different sales channels, anticipate the impact of your decisions on consumer demand, and to bring a unified view of product and service demand to all stakeholders.
After your first demand forecast model is implemented, the process of demand forecasting does not stop. Real-time data is organic, changing quickly based on new customer data. Consequently, your newly-built forecasting infrastructure needs to quickly adapt to these changes so that your models are accurate. With Data Science Studio, the process of retrieving new data and updating forecasts is automated — your models automatically take into account all of the information at hand and outputs are updated accordingly.
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