Optimizing Operational Spend by Predicting Product SalesGroup B3Aditya Verma - 61910756Amit Kumar Gupta – 61910279Neeraj Nathany – 61910791Prateek Singhvi - 61910308Varsha Shridhar - 61910351Vishal Abraham – 61910217

FCAS1_B

• Client: Retail Businesses

• Challenge/Opportunity

• To Plan Production activities and Inventory Management based on Demand Forecasts

• Facilitate Manpower Planning based on peak and lean demand cycles

• Reduce Overall Costs

• Client Use: Optimize operational costs at store and product using the proposed forecast model

• Cost of error: Inventory management cost in case of over estimation and loss to business opportunity in case of under estimation

Business goals

1. Forecasting overall yearly and quarterly sales for 2018 of 2 products using 16 quarters of training data and 4 quarters of validation data

2. Forecasting monthly sales for 2018 of 2 products using 48 months of training data and 12 months of validation data

3. Forecasting daily sales for first 2 months of 2018 for 2 stores using 58 months of training data and 2 months of validation data

Data source: https://www.kaggle.com/c/demand-forecasting-kernels-only/data

Forecasting goals

• We have 5 years and 3 months (2013 onwards) of time series data at daily level for sales of 50 products at 10 store locations

• The dataset has 9,58,000 records and consists of categorical variables such as store id and product id and continuous variables such as sales

• To build the model 5 years of data would be used as training dataset while latest 3 months of data would be used as test dataset since 3 months is the forecast horizon

Data Processing and Data Charts• Data Cleaning – Few data points were missing in the

data for which we assumed means of the population as placeholder values for them

• Exploration – We viewed many cross sections and segments of the data to look for levels, trend and seasonality

Data DescriptionOverall Yearly Sales Trend

Monthly Sales Trend Across Years

Monthly Sales Trend Across YearsFor A Given Product and Store

Quarterly series for Item 5 & 15

Monthly series for Item 5 & 15

Daily series for Store 2 & Store 7

Training: Prediction Summary Validation: Prediction Summary

Metric Value Metric Value

SSE 11420459 SSE 705093.3

MSE 6466.851 MSE 11751.56

RMSE 80.41673 RMSE 108.4046

MAD 63.54618 MAD 93.30771

R2 0.968024 R2 0.922118

Linear Regression Output

Error Measures: Training

Record ID Value

SSE 9328352

MSE 9717.033

MAPE 3.872437

MAD 71.86579

CFE -190.966

MFE -0.19892

TSE -2.65725

RMSE 98.57501

Error Measures: Validation

Record ID Value

SSE 10637253.2

MSE 265931.331

MAPE 29.3046096

MAD 459.255529

CFE -17487.9831

MFE -437.199578

TSE -38.0789823

RMSE 515.685302

Holts Winters Smoothing Output

Partitioning:

1. 16 Quarters + 4 Quarters

2. 48 months + 12 months

3. 912939 days + 61 days

Metrics of Interest:

1. MAPE and RMSE

2. Forecasted values

Benchmark:

1. Naïve model – Status Quo

Comparison:

Comparison against each of the different models to forecast

Evaluation

Recommendations

• Daily forecast for the 2 stores will be helpful in staffing and planning no. of counters to be open on which day of the week

• Monthly forecasts for the 2 products in a particular store will help determine the inventory levels and order levels

• Quarterly or annual forecasts will help in capacity building at the aggregate production level

Thank you Group B3Aditya Verma - 61910756

Amit Kumar Gupta - 61910279

Neeraj Nathany - 61910791

Prateek Singhvi - 61910308

Varsha Shridhar - 61910351

Vishal Abraham – 61910217