1 7178835943 | [email protected]

ICBDASCM · Registering as Listener

International Conference on Big Data Analytics for Supply Chain Management

27th Nov – 28th Nov 2026 Crete, Greece Standard / Physical Participation

Listener Registration From

$—

$— in person

Registration Benefits:

Official invitation letterIssued automatically after registration

Certificate & digital materialsGet certificate, slides and resource materials

Supporting global researchConnect with researchers across 30+ countries

Select registration mode

Prices are shown before tax and bank charges — no surprises at checkout.

Standard / Physical $—

On-site keynotes, talks, workshops, networking, conference kit and certificate.

Select

Virtual $—

Live streaming access to all sessions, digital materials and certificate.

Select

All sessions Networking Certificate Invitation letter Conference kit

Amount:

Your details

We only need what's required to register and email your confirmation. Everything else is optional.

Full Name *

Email Address *

Mobile Number *

City *

State *

Country *

Zip / Postal Code

Address

Credit Card

Debit Card

Net Banking

Cash Card

Unified Payments

For Support Please Contact

[email protected]

Coupon code

Have a code? Apply it here — the discount updates the total immediately.

Apply

I agree to the Terms & Conditions and Refund & Cancellation Policy

VISA MC AMEX UPI

Payments encrypted & processed securely. Refundable up to 14 days before the event.

Registration summary

ConferenceICBDASCM

ModeStandard / Physical

ParticipationListener

Registration fee$—

Bank charges (5.8%)$—

Discount-$0.00

Total payable $—

Includes all bank processing charges — the amount above is exactly what will be charged. View charge breakdown

Need help?

Contact our registration team:

1 7178835943

[email protected]

Benefits of Registering as Listener

Access to Conference Sessions

Networking Opportunities

Certificate of Participation

Invitation Letter Support

Conference Kit / Materials

Access to Keynote Sessions

Conference Session Tracks

SDG-Aligned Research Themes

International Conference on Big Data Analytics for Supply Chain Management conference tracks support global knowledge exchange, innovation, and sustainable development priorities across diverse disciplines.

SDG 9 - Industry, Innovation and Infrastructure

SDG 11 - Sustainable Cities and Communities

SDG 12 - Responsible Consumption and Production

This track focuses on innovative approaches to predictive modeling within supply chain contexts. Researchers are invited to present methodologies that enhance demand forecasting and inventory optimization through advanced analytics.

This session explores the application of supervised learning algorithms to solve complex supply chain challenges. Contributions should demonstrate how these techniques can improve decision-making processes and operational efficiency.

This track addresses the use of unsupervised learning methods for identifying anomalies in logistics and supply chain operations. Papers should highlight novel approaches to feature extraction and risk assessment.

This session invites research on the integration of deep learning techniques in optimizing supply chain processes. Topics may include production planning, resource allocation, and predictive maintenance.

This track examines the role of IoT analytics in improving supply chain transparency and responsiveness. Submissions should focus on case studies or frameworks that leverage IoT data for better decision-making.

This session emphasizes the importance of data-driven strategies in supply chain management. Researchers are encouraged to present frameworks that facilitate effective decision-making through analytics.

This track explores the latest trends and innovations in logistics analytics. Contributions should address how data analytics can enhance logistics performance and efficiency.

This session focuses on methodologies for risk assessment and management in supply chains using big data analytics. Papers should discuss frameworks that identify, analyze, and mitigate risks effectively.

This track invites research on advanced feature extraction techniques tailored for supply chain data. Submissions should demonstrate how these techniques can enhance model performance and insights.

This session highlights the role of predictive analytics in optimizing inventory management practices. Researchers are encouraged to share insights on improving stock levels and reducing costs through analytics.

This track examines the integration of big data analytics into overarching supply chain strategies. Contributions should focus on case studies that illustrate successful implementations and outcomes.