Here's how you can forecast demand using predictive analytics in business operations.

Here’s how you can forecast demand using predictive analytics: 1. Data collection: Include historical sales data from all channels, customer demographics, product categories, etc. Overlay other factors such product launch, promotions, seasonality, holidays, etc. 2. Data processing: Data may need cleaning, identifying & filtering outliers, missing values, consistent formatting etc. 3. Forecasting Model: Different models may be selected based on scenarios, example – Regression models, Time series models, ML models, etc. 4. Performance monitoring & Refinement: Model performance is tracked for accuracy and model is refined for better accuracy.

# To create a predictive model, one must go through the existing articles to grasp the fundamentals of all pros & cons. # Mostly the regression models or Neural Network models can come up with the best forecasting accuracy. # Model needs to be chosen based on the complexity of data and the historical trend. # The model should be able to handle the data in terms of computational efficiency in all aspects. # In Neural Networks, the non-iterative techniques like Extreme Learning Machines (which follows regression) can give fast execution. The learning parameters get tuned automatically without optimising them. # The time-lag inputs( from ACF & PACF) are important while training & testing the dataset. (60% data- Training, 40%- Testing)

** To validate any forecasting model, the predicted target must be compared with the actual value. ** To do so, the errors must be calculated. Mean Absolute Percentage Error (MAPE) is widely adopted in this regard. Some others: MAE, MASE, MSE etc.. ** The error must be as low as possible (1-4% for a good forecasting model). ** The parameters in the model need to be modified looking into the prediction error. ** Statistical properties of data are very important while feeding the data into any forecasting model. Accordingly the inputs can be revamped as and when desired.

To forecast demand using predictive analytics in business operations, data of past transactions are analysed to predict future demand. It calls for valid and reliable historical data to ensure that future plans and execution can be effective in business operations.The data will show your strength and weaknesses and indicate the effective ways to improve on your weaknesses inorder to provide better performance on your future engagement.