ArticlesOpen Access

Leveraging AI for Data Integration in Optimized Supply Chain Management

DOI: 10.18535/mcsj/v2023.07· Pages: 226-245· (2023)· Published: July 21, 2023
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Abstract

Global supply chain has become complex and thus there is always need to look for new and effective ways that will help improve the efficiency, transparency and also the strength of the supply chain. In today’s supply chain management, artificial intelligence or AI has therefore taken on the role of a disruptive technology especially in the management of data integration. Machine learning, predictive analytics, and natural language processing technologies when applied can enhance business functioning, decision making processes and help to identify disruptions in near real time.

This paper aims to establish how AI is shaping supply chain management by progressing data integration approaches to unify systems, apply real-time analytics, and increase supply chain transparency. The chief use areas are inventory level management, demand forecasting, procurement planning, and logistic management as these functions are vital for cost cutbacks and operational efficiency.

However, the following issues are worth exploring: Data silos, integration issues, and ethical concerns, the following solutions: Use of blockchain, federated, learning, and unifying AI platforms. Examples from a flourishing retail to a struggling manufacturing plant and a logistics company are highlighted and great performance milestones of AI-oriented policies are revealed to be incomparable to traditional models.

These outcomes prove the significance of using AI technologies for meeting new requirements of modern supply chains and gaining benefits. Expanding opportunities of AI in SCM are presented as well as possibilities of the further use of quantum computing and other innovations based on sustainability, which constitute a set of directions on the transformation of the SCM environments.

Keywords

AIdata integrationsupply chain managementmachine learningpredictive analyticsnatural language processing (NLP)real-time analyticsinventory optimizationdemand forecastingprocurement managementlogistics coordinationblockchain in supply chainfederated learningunified AI platformsdata silosoperational efficiencysupply chain resiliencecost reductiondigital transformationglobal logisticssmart warehousingautonomous systemssupply chain visibilitydisruption predictioncloud computingbig datadigital twinsedge computingIoTsmart contractssustainabilityquantum computingadaptive algorithmssupply chain optimizationvendor managementtransportation planningethical AIdata standardizationrisk managementagile supply chainsscenario planningcollaborative AI systemsenhanced decision-makingrobotic process automation (RPA)augmented analyticsanomaly detectiondecentralized networksgreen logisticslast-mile deliverydemand-supply alignment.

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Author details
Narendra Devarasetty
Doordash Inc, 303 2nd St, San Francisco, CA 94107
✉ Corresponding Author
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