- SAP Managed Tags:
The following blog post is a submission for SAP Business One SEED Development Challenge.
Predicting Fashion Trends using SCP Solution Demo Video:
Solution Name:
Predicting Fashion Trends using SAP Cloud Platform
Solution Description:
The fashion industry has been known for being extremely exclusive to everyone but the insiders. It is difficult for every day consumers to understand what really goes on within the fashion industry. Traditional media further reiterated this exclusivity. Today, focus in the fashion industry has drifted toward digital media, specifically fashion blogs and social media.
Fashion & trend forecasting is the prediction of mood, behavior and buying habits of the consumer at particular time of season. Understanding fashion trends forecast is one of the most arduous and calculative work for all
The solution proposed helps fashion designers, buyers and merchandisers to upload, share and analyze styles for upcoming seasons using latest technology available.
Fashion companies face several issues when it comes to predict trends and forecast new products:
These are metrics based on actual surveis in Fashion comapnies
Persona Identified:
Web IDE, Xamarin forms (Mobile), Fiori Launchpad, Ui5 Controls,
Picture: Fiori Launchpad with three apps (Moodboards, Style list and model detail).
Picture: Mater Detail view with styles showing sentiment analysis from social networs (pinterest and facebook) and Forecast using predictive services based on sales profiles - since fashion products have no sales history.
SAP Cloud Platform , AWS (S3)
Machine Learning - Visual Classification API
Pinterest and facebook APIs , Predictive and sentiment libraries from SCP , Portal (Fiori Launchpad)
Industry focus:
Fashion Companies - Merchandise Planning
Marketing strategy:
The time it takes for a trend to disseminate into mass market products has dramatically reduced the past several years. With this pressure comes an immense need to reduce cycle time and deliver innovative products to maintain competitiveness.
This solution will be an extension for SAP Business One customers running the AFS solution. As this is decoupled from SAP B1 it can also run as standalone app.
Road Map
IOT to give identification to garments and integration to fashion services to analyze global trends. Full integration with SAP Leonardo APIs.
Contact Information
Partner: Argentis Systems
Country: UK , Argentina, US
Team Name: Lucas Ritondale
Team Members: Dario Salas, Jose Espindola, Lucas Costantini
Predicting Fashion Trends using SCP Solution Demo Video:
Submission Details
Solution Name:
Predicting Fashion Trends using SAP Cloud Platform
Solution Description:
The fashion industry has been known for being extremely exclusive to everyone but the insiders. It is difficult for every day consumers to understand what really goes on within the fashion industry. Traditional media further reiterated this exclusivity. Today, focus in the fashion industry has drifted toward digital media, specifically fashion blogs and social media.
Fashion & trend forecasting is the prediction of mood, behavior and buying habits of the consumer at particular time of season. Understanding fashion trends forecast is one of the most arduous and calculative work for all
The solution proposed helps fashion designers, buyers and merchandisers to upload, share and analyze styles for upcoming seasons using latest technology available.
Solution Use Case
Fashion companies face several issues when it comes to predict trends and forecast new products:
- Low predictability: because of the volatility of demand it is extremely difficult to forecast (new products with no sales history)
- Short selling season: today’s fashion market place is highly competitive and the constant need to ‘refresh’ product ranges
- Users need to predict and spot which colors, cuts and shapes are going to hit the streets
These are metrics based on actual surveis in Fashion comapnies
Persona Identified:
Pain Points
- Low predictability: because of the volatility of demand it is extremely difficult to forecast
- Short selling season: today’s fashion market place is highly competitive and the constant need to ‘refresh’ product ranges
- High impulse purchasing: many buying decisions by consumers for these products are made at the point of purchase
Solution Detail
- - Ideas are instantly upload the SAP Cloud Platform to be shared with the merchandise team
- - Styles recognized (SAP Leonardo Visual APIs - Amazon Recognition)and categorized in boards (Pinterest) and moodboards (SCP) for analysis (no manual interventions)
- - Images shared in Pinterest and other social networks to gather instant trends.
- - SCP predictive services + sales profiles to analyze trend forecasting to rank styles
- - Top styles are selected and post in SAP B1 On Premise – Cloud company
Solution Technology
Web IDE, Xamarin forms (Mobile), Fiori Launchpad, Ui5 Controls,
Picture: Fiori Launchpad with three apps (Moodboards, Style list and model detail).
Picture: Mater Detail view with styles showing sentiment analysis from social networs (pinterest and facebook) and Forecast using predictive services based on sales profiles - since fashion products have no sales history.
SAP Cloud Platform , AWS (S3)
Machine Learning - Visual Classification API
Pinterest and facebook APIs , Predictive and sentiment libraries from SCP , Portal (Fiori Launchpad)
Go-to-Market Strategy
Industry focus:
Fashion Companies - Merchandise Planning
Marketing strategy:
The time it takes for a trend to disseminate into mass market products has dramatically reduced the past several years. With this pressure comes an immense need to reduce cycle time and deliver innovative products to maintain competitiveness.
This solution will be an extension for SAP Business One customers running the AFS solution. As this is decoupled from SAP B1 it can also run as standalone app.
Road Map
IOT to give identification to garments and integration to fashion services to analyze global trends. Full integration with SAP Leonardo APIs.
Contact Information
Partner: Argentis Systems
Country: UK , Argentina, US
Team Name: Lucas Ritondale
Team Members: Dario Salas, Jose Espindola, Lucas Costantini
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