Forecasting swimsuit sales for the next month to assist inventory management for Heatwave

Team 4 :Jheng Kai-Ru (107078503) Adam Yu (107078506) Silvia Yang (107078507) Zoly Chang (107078509)

Heatwave is a B2C swimsuits seller on a Chinese e-commerce platform called TMall. They design and manufacture their own swimsuits

Sell on

Problem

Heatwave has limited knowledge toward decision on how many product to produce; also to put promotions on certain products. They mostly do it base on their experiences. Sometimes it works, sometimes it doesn’t.

Client

Goal

Using the historical sales data to forecast the sales of the next month to assist with their inventory management and production strategy.

Business Problem

Data Description: Data Constraints

● Data from Shen-Yi-Can-Mou is too short!● Order doesn’t match in order list and item list● Some products may have different product name, and

may have different product id● Two sources of data have different product id● We miss 1314 data when integrated them

Problems

Although the data are not very accurate, we think it will still be helpful for our forecasting : )

From 2017-05 to 2018-11Summer has higher sales

Data Description: After Preprocessing

Monthly Data

From 2017-04-22 to 2018-12-18Holiday has higher sales

Daily Data

Data Description: Original Data

● Data : Monthly and daily Sales data● Time Period :

Daily 2018/07/25 - 2018/12/18 Monthly 2017/09 - 2018/11

● Data Quality : good

● Data : Daily data, contain order list and item list

● Time Period : 2017/04/22 - 2018/11/24● Data Quality : bad

Shen-Yi-Can-Mou

TMall’s Analytic Platform

Order # of products

1 3

2 2

3 1

Order Product ID

1 82275

1 84567

1 87632

2 82275

2 87632

Order List Item List

Compute date Product ID Product Name # of payments $ of payments

Data Preprocessing TMall Analytics

Order List Item List

Separate product names into one row

Find the product names and product id, if there has same name

but different id, change the id.

Join two list

If there has pId

Find the pId from pName

We cannot know which product it is

If there has pId

Aggregate sales data into daily and monthly data frame

Sheng-Yi-Can-Mou Data

Merge into the data frame

Forecasting data

No

Yes

Yes

No

We have 1314 row data don’t have pId, so we ignore it.

Method: Forecast Monthly Data

P82275 monthly forecast error (RMSE)

Model Training error Test error

sNaive 336.8237 93.0430

regression 129.15176 76.34134

arima 289.2212 222.6107

Ensemble 251.7322 130.665

Method: Forecast Daily Data

P82275 daily forecast error (RMSE)

Training error Test error

sNaive 11.582822 2.632218

ets 8.317506 2.138090

arima 8.317509 2.138103

Ensemble 9.405945667 2.302803667

EvaluationMonthly (RMSE) Daily (RMSE)

sNaive(Benchmark)

121.4645 4.53995822

38.87665 1.50777218

ets131.9892 3.315389

174.2751 1.00362794

regression40.12895 5.535291

41.63898 3.6440104

arima102.0755 3.294651

99.33205 0.98115324

Ensemble(top 3 model)

87.88965 3.716666073

59.9492265 1.164184453Over-forecast!

Overfitting!

Recommendation1. sNaive for monthly, ets regression for daily.

2. Retain their data autonomy, and confirm the data quality additionally.

3. Compare the similarities.

4. Data long enough inventory management reaching lean production.

5. Forecasting + Domain Knowledge

Constraint1. Forecasting future clothing trend is hard when solely using the data.2. Short product life cycle.3. Data constraint

Monthly

Recommendations & Limitations