The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Big Data in Supply Chain Management Demand Forecasting and Inventory Optimization
|
|
Author(s): Boutrous Khamis Juma (Marwadi University, India), Rituraj Jain (Marwadi University, India), Aaron F. Cooper (Marwadi University, India), Romanzi Zulu (Marwadi University, India), Damodharan Palaniappan (Marwadi University, India), Kumar J. Parmar (Marwadi University, India)and T. Premavathi (Marwadi University, India)
Copyright: 2026
Pages: 28
Source title:
Transforming Sustainability and Value Creation with Logistics and Supply Chain Management
Source Author(s)/Editor(s): Saurav Negi (Modern College of Business and Science, Oman)and Reason Masengu (Middle East College, Oman)
DOI: 10.4018/979-8-3373-0649-0.ch001
Purchase
|
Abstract
This chapter discusses how Big Data influences SCM, with focus on areas like demand forecasting, inventory control, and newer technologies. With real-time data, AI, and machine learning, improved forecasting, reduction of risks regarding overstocking or stockout, and better satisfaction of customers are all possibilities. Other advantages are cost reduction and better control over inventories. Market adaptation is driven by IoT and real-time data. While big challenges remain, including the poor quality of data, resistance to change, there is promise for SCM using big data through predictive analytics, collaboration, and ethical practices.
Related Content
|
Aditi Nag.
© 2026.
48 pages.
|
|
Mayur Thakur, Shikha Sharma, Trilochan Kumar.
© 2026.
44 pages.
|
|
Partha Mukhopadhyay, Prachee Parwanee.
© 2026.
36 pages.
|
|
Kamaraj Kalaimathy, Chathana Thagavel, Sofiya M. Karunanithi.
© 2026.
30 pages.
|
|
İlhami Ay, Murat Dal.
© 2026.
34 pages.
|
|
Vinupandyan Lakshmanan.
© 2026.
32 pages.
|
|
Muhammad Usman Tariq.
© 2026.
28 pages.
|
|
|