This document summarizes an analysis conducted to forecast sales for the next 6 months to help with inventory management for a global retail store. The analysis identified the APAC consumer market segment as the most profitable based on sales, quantity, and profit coefficients. Thirteen forecasting models were evaluated and the Holt Winters' additive method was found to perform best. This model was then used to generate a 6-month sales forecast for the APAC consumer segment, predicting a general increase in sales with a steeper rise from April to June 2015.

1. Retail Giant
Sales
Forecasting
Assignment
SUNIL KUMAR GUPTA

2. Global Mart store takes online orders and delivers across the 7 global regions and deals with consumer, corporate
and home office product categories

3. The store wants to finalize the inventory management plan for the next 6 months.
Hence, the objectives of the analysis are:
• Find out the most profitable (and consistent) market segments for the company.
• For these segments, forecast the sales and the demand for the next 6 months, so that the revenue and
inventory may be managed accordingly
The analysis has been divided into four parts:
• Data Understanding
• Finding the most profitable segments
• Forecasting sales and demand for each of the profitable segments
• Recommendations for inventory management

4. Data Preparation Modelling Model Evaluation Forecasting
• Create 21 Market Segment
buckets
• Aggregate buckets by Sales,
Profit, Quantity
• Calculate Coefficient of
Variation(CV)
• Pick Top 2 Segments based on CV
& Profit
• Aggregate data by Market-
Segment & Order Month on
subset of most profitable market
segment
• Create time series of top
Aggregated Data
• Smoothen time series to identify
trend & seasonality
• Creating train & validation sets of
size 42 & 6 months
• Build Different Models
• Evaluate model on validation set
using RMSE & MAPE
• Choose best model out of all the
Models using MAPE
• Use best model to forecast future
6 months Sales for the most
profitable market segment

5. • We can see that Canada and APAC markets with the segments Consumer, Corporate and Home office have more profit
• Africa and EMEA markets have less profit
• APAC market having higher Sales in all the three segments
• EMEA and Africa have less sales

6. • We can see from the plot APAC_Consumer has the highest count

7. 1. Created 21 data subset buckets based on Market & Segment they
belong
2. Aggregated data in each bucket by Sales, Quantity & Profit
3. Calculated Coefficient of Variation(CoV) using aggregated Profit
for each Market-Segment using below:
CoV = std(Profit)/mean(Profit)
4. Using CoV & Profit found most profitable Market-Segments as
APAC_Consumer with below values

8. Decomposed the data using
additive method:
• There is clear upward
trends
• There is a yearly
seasonality in the data

9. Decomposed the data using
multiplicative method:
• There is clear upward
trends
• There is a yearly
seasonality in the data

17. Using ADF and KPSS test we identified that time series
data is not stationary.
Since Data was not stationary we need to do Box Cox and
Differentiating to make it Stationary

18. • From ACF plot we could see the
dependency on the very next
node which means MA should
be 1
• From PACF plot we could see
the there is a seasonality in the
data

23. Using AutoArima from pdarima library following best parameter we are getting: SARIMAX(0, 1, 1)x(1, 0, 0, 12)

25. Using AutoArima from pdarima library following best parameter we are getting: SARIMAX(0, 1, 1)x(1, 0, 0, 12)

27. The best performing time series model is Holt Winters’ Additive method out of 13 time series models

28. Forecasting for future 6 months sales for months Jan 2015 to Jun 2015

29. 1. Based on data provided we helped “Global Mart” in identified most profitable market segments as APAC Consumer
2. We created total 13 forecasting models for APAC Consumer market segment
3. Selected the best performing time series model as Holt Winters’ Additive method
4. SARIMA - Seasonal Autoregressive Integrated moving average is the best method in ARIMA set of techniques.
5. Generated forecasting for future 6 months sales for months Jan 2015 to Jun 2015
6. Below is summary of key forecasts on test data (Jan 2015 – Jun 2015):
I. APAC Consumer Sales is likely to rise in next 6 months with small fluctuations
II. APAC Consumer is also likely to rise steeply from Apr 2015 to Jun 2015