Product demand forecasting service for the apparel industry

We analyze product data and weather data to provide insight into the relationship between temperature and timing for optimal sales per item.
It’s possible to forecast the demand for a specific item. First, tell us about your challenges.

case01Analyzing the relationship between weather and products (weather sensitivity survey)

Challenge:“First I need to know which products are weather-related.”

We analyze the sales data of multiple products versus weather data to visualize the relationship with weather, by timing and area.

Sells more in hot weather
Not affected by temperature
Sells more in cold weather

Use it to roughly understand the relationship between multiple items and the weather.

case02Visualizing the peak period

Challenge:“I need to know when a product will start to sell, peak, and taper off.”

We will summarize the timing of the product's “beginning of sales,” “peak” and “end of sales” cycle, as well as the benchmark temperature that triggers buying behavior, by area.

Leverage it for marketing and promotion purposes.

JWA weather forecasts and email alertsさらに詳しく!

By checking the weather forecast together with the results from the above analysis, you can understand the beginning,
peak and end of sales cycle for this year.

case03Forecasting demand

Challenge:“I want to predict how much quantity I can sell.”

We will perform analysis by matching the detailed product data such as POS, shipment volume, price and inventory, against weather data.
It is possible to forecast specific quantities and sales amounts.

Units sold
Past Current Future
350個予測 500個予測 過去のデータから未来を予測! 350個予測 500個予測 過去のデータから未来を予測!

Use it to adjust production volume, shipment volume, inventory volume, and for marketing.

case04Factor analysis

Challenge:“I want to understand the factors that affected sales in the past.”

We analyze past sales factors from the perspective of weather and economic factors to see which factors are critical. Objectively visualizing which factors have previously influenced sales will lead to more effective measures for the future.

前年より売上が増えた要因は? どのくらいの影響度があるの? 前年より売上が増えた要因は? どのくらいの影響度があるの?
Rank Factor Degree of influence
1 Price 40%
2 Temperature 30%
3 Snow 20%
4 TV commercial 10%

Use it as a resource when considering future measures.

Anticipated effects

01

Reduction of wasteful inventory

02

Avoiding out-of-stock items

03

Efficient production planning

04

Maximizing sales
by timing price reductions

05

Optimizing the timing of
advertising promotions

06

Sales activity support

Product demand forecasting service for the apparel industry

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