Data analytics and demand forecasting go hand in hand. They are essential tools for businesses and organizations to make informed decisions and stay ahead of the competition. Together, they can be used to help organizations make the best business decisions in terms of supply chain, production, and operations management. Using data analytics and demand forecasting together, business development teams, operations, and marketing teams are gaining in-depth insights into their business. Here, we will explore the basics of data analytics and demand forecasting techniques and how they can be used to improve decision-making and drive business success.
Leverage our demand forecasting solutions for better sales & inventory management
Data analytics are increasingly being used as a tool to make critical business and investment decisions. It involves examining and interpreting large sets of data to uncover patterns, trends, and insights. This information can then be used to make informed decisions and optimize business processes. The volume of data is growing faster than the ability to manage it and leverage it appropriately, and this is resulting in an explosion of new data analytic technologies.
What is demand forecasting?
Demand forecasting is the process of estimating the future demand for a product or service. This information can be used to make informed decisions about production, inventory management, and sales strategies. It involves analyzing historical data, considering current trends and market conditions, and using statistical models to make predictions about future demand.
Data analytics is essential for demand forecasting because it provides the information needed to make accurate predictions about future demand. By analyzing large sets of data, businesses and organizations can uncover patterns and trends that can inform their demand forecasting models. Data analytics also allows businesses to identify and address potential issues, such as seasonal fluctuations or changes in market conditions, that may impact demand.
Demand forecasting techniques for driving business success
Demand forecasting can help businesses make informed decisions about production, inventory management, and sales strategies. By accurately predicting future demand, businesses can ensure they have the right products in stock and avoid overstocking or stock shortages. This can help to improve customer satisfaction, reduce costs, and increase profitability.
Some common demand forecasting techniques include time-series analysis, regression analysis, and machine learning algorithms. By using a combination of these techniques, businesses can gain a comprehensive understanding of future demand trends and make data-driven decisions that drive growth and increase profitability. Whether you are launching a new product, expanding into new markets, or simply trying to optimize your existing operations, demand forecasting is an essential tool for success.
At Niveus, we use multiple demand forecasting techniques, including –
Smoothed Moving Average: This is useful for looking at overall sales trends over time and aiding long-term demand planning. Rapid changes as a result of seasonality or other fluctuations are smoothed out so you can analyze the bigger picture more accurately. This typically works well when you have a product that’s growing consistently or declining over time and if you want to do a long term forecast of around 12-24 months.
Holt Winters Exponential Smoothing: This works well if you want to capture the seasonality of your products. It is a time series forecasting method that combines both trend and level information in order to forecast future values. It can be used to forecast data with both upward and downward trends. It uses a weighted average of past observations to calculate the predicted values, with more recent observations given more weight in the average. You can use this if you want to do a short term forecast of around 3 to 12 months.
ARIMA(Auto Regressive Integrated Moving Average): This is actually a class of models that ‘explains’ a given time series based on its own past values, that is, its own lags and the lagged forecast errors, so that equation can be used to forecast future values. It can be used in non-seasonal time series that exhibits patterns and is not a random white noise. The model can be used to forecast future values of a time series by fitting the parameters to the historical data and using them to make predictions. ARIMA models are widely used in econometrics, finance, and other fields where time series data is collected and analyzed.
Niveus’ demand forecasting solutions
Our demand forecasting solution is a cutting-edge tool that helps businesses stay ahead of the curve by predicting future trends in demand. By analyzing historical data, the solution is able to identify patterns and make accurate predictions about future demand volumes. This information is then used to help businesses manage fluctuations in demand, ensuring they always have the right amount of inventory on hand to meet customer needs. Whether you’re a small retail store or a large multinational corporation, our demand forecasting solution can help you stay ahead of the game and make informed decisions that drive success. Here are a few of our work with businesses across industries –
- We built an integrated call volume prediction system that forecasts call volumes for a leading custom Business Process Management service provider across various business verticals such as retail, BFSI, and telecom. The highly scalable solution runs multiple predictions with improved accuracy and reduced 20% in the prediction time
- We helped a major automotive manufacturer to analyze their data from their dealer management system and CRM applications to forecast sales and increase business agility
In conclusion, data analytics and demand forecasting are essential tools for businesses and organizations looking to make informed decisions and drive success. By analyzing large sets of data, businesses can uncover patterns and trends that inform their demand forecasting models, allowing them to make accurate predictions about future demand. This information can be used to optimize production, inventory management, and sales strategies, resulting in improved customer satisfaction, reduced costs, and increased profitability.
