Supply Chain Collaboration Support Systems

Data visualization and Forecasting Models for Supply Chain Collaborative Processes

Example collaborative processes include sales and operations planning, collaborative planning forecasting and replenishment.

• Data visualization: Dashboard-based decision support system supply chain collaboration processes
• Machine Learning: Improved forecasting accuracy
• IoT Sensory Data / Text Mining: Additional supply/demand sensing information integrated to decision making

• Decision support or recommendation systems for KPI monitoring.
• Machine learning models can process big data and improve forecasting accuracy.
• IoT sensor data can be integrated to provide real-time inventory tracking, supply disruption information.
• Voice of customer, user product reviews can be analyzed through natural language processing to supplement demand information.

 

Supply Chain Collaborative Projects

• Prior supply chain collaborative projects (S&OP, CPFR) yielded benefits for manufacturers and their supply chain partners
• Collaboration leads to richer data which typically leads to a 30-40% improvement in forecast accuracy
• These processes can be further enhanced by Machine-Learning, text mining algorithms. (Estimated forecast accuracy improvement by additional 5-10%)

Data-Driven Dashboards

• Development of data-driven dashboards with ML features can support decision making and maximize benefits from supply chain collaboration initiatives

-AMR Research (2001) "Beyond CPFR: Collaboration Comes of Age," The Report on Retail E-Business, April 2001
 
-Tableau (2016) "PepsiCo cuts analysis time by up to 90% with Tableau + Trifacta."