Forecasting swimsuit sales for the next month to assist inventory - - PowerPoint PPT Presentation

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
• wn 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

• n 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 2018-11 Summer has higher sales

Data Description: After Preprocessing

Monthly Data

From 2017-04-22 to 2018-12-18 Holiday 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

Evaluation

Monthly (RMSE) Daily (RMSE) sNaive (Benchmark) 121.4645 4.53995822 38.87665 1.50777218 ets 131.9892 3.315389 174.2751 1.00362794 regression 40.12895 5.535291 41.63898 3.6440104 arima 102.0755 3.294651 99.33205 0.98115324 Ensemble (top 3 model) 87.88965 3.716666073 59.9492265 1.164184453 Over-forecast! Overfitting!

Recommendation

1. 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

Constraint

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

Monthly

Recommendations & Limitations