Forecasting Analytics. Group members: - Arpita - Kapil - Kaushik - Ridhima - Ushhan

Transcription

1 Forecasting Analytics Group members: - Arpita - Kapil - Kaushik - Ridhima - Ushhan

2 Business Problem Forecast daily sales of dairy products (excluding milk) to make a good prediction of future demand, and predict the stock level required to meet the demand. Evaluate different forecasting methods on data distribution and forecast period, and pick the best one based on the results. Dairy Products Ice Cream Lassi Srikhand Cups & Cones Family Packs Family Packs Family Packs 2

3 Ice Cream & Gelato Analysis & Forecast 3

4 Ice Cream Sales ( ) 31 st March 1 days before Mahavir Jayanti Good Friday A day before Raksha Bnadhan Gandhi Jayanti 2 days before Muharram Valentine s day Budha Purnima 4

5 Data Visualization- Sales Distribution Across subclasses Cups & Cones - Monthly Week day seasonality Family Pack - Monthly 5

6 Analysis & Forecast Sales ( Cups and Cones ) Regression ( Single Seasonality) Moving Average Method Holts Winter Method MAPE 0.89 RMSE MAPE 0.58 RMSE MAPE 0.69 RMSE

7 Analysis & Forecast Sales ( Cups and Cones - Continued) Regression Polynomial Trend and Multiple Seasonality ( Weekly and Half Yearly) The Regression Model Input variables Coefficient Std. Error p-value SS Constant term t t ^ day of week_ day of week_ day of week_ day of week_ day of week_ day of week_ month of year_ Training Data scoring - Summary Report Total sum of squared errors RMS Error Average Error Validation Data scoring - Summary Report Total sum of squared errors RMS Error Average Error MAPE MAPE improved

8 Lassi & Srikhand 8

9 Data Visualization-Weekly Demand 9

10 Daily Quantity Sold Data 10

11 ACF Correlation between daily sales 1 ACF Plot for Sum of Quantity_Sold Lags ACF UCI LCI Next day related to the previous day and a week before 11

12 ACF Naïve Forecast ACF Plot for Residual MAPE 130% Lags ACF UCI LCI Negative correlation between a day's sales and sales previous day? Using Naïve we are just taking previous day and forecasting but it seems lot of signal is not captured It says that naïve forecasting model is not able to explain 12

13 Forecast using Multiple Regression Input variables Coefficient Std. Error p-value SS Constant term Row Labels Weekday_Mon Weekday_Sat Weekday_Sun Weekday_Thu Weekday_Tue Weekday_Wed Residual df 321 Multiple R-squared Std. Dev. estimate Residual SS MAPE: 133% Training Data scoring - Summary Report Total sum of squared errors RMS Error Average Error E-06 Validation Data scoring - Summary Report Total sum of squared errors RMS Error Average Error

14 ACF PACF ACF & PACF with Multiple Regression ACF Plot for Residual PACF Plot for Residual Lags ACF UCI LCI -1 Lags PACF UCI LCI Regression model tells us that next day s sales are dependent on last two days sales along with regular time component 14

15 Sum of Quantity_Sold Sum of Quantity_Sold Smoothing Moving Average(7) Time Plot of Actual Vs Forecast (Training Data) Time Plot of Actual Vs Forecast (Validation Data) Actual Row Labels Forecast Actual Row Labels Forecast Error Measures (Training) MAPE MAD MSE Error Measures (Validation) MAPE MAD MSE Shows strong correlation between days sales with last 2 days It also tells that moving average could be used in roll forward manner 15

16 Holt Winter Forecasting Method-Additive Error Measures (Training) MAPE MAD MSE Error Measures (Validation) MAPE MAD MSE

17 Moving Average (MA2) MAPE MAD MSE MAPE MAD 16.5 MSE Model Fits quite well but can only forecast for next 1-2 days 17

18 Holt Winter Smoothing-Additive Error Measures (Training) MAPE MAD MSE Error Measures (Validation) MAPE MAD 16.5 MSE

19 Conclusions & Suggestions Different in forecasting methods used for effective forecast Recommendations to Business Stock level on weekly or monthly basis can be predicted for dairy products. ERP system that could directly tell the vendor how much to deliver Model is useful to predict for next day given previous two days sales, need to implement roll forward 19

20 THANK YOU 20