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Get Ready for Success! 4 Critical Workstreams for Successful Procurement Implementation

ivalua

Up front efforts to define roles and responsibilities, document requirements, integrate with other enterprise systems, and maximize the value of data will be rewarded in multiple forms well beyond the conclusion of the implementation.

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Conquering the Explosion of Supply Chain Planning Data

Logility

Building a digital twin involves capturing, standardizing, blending and modeling both structural supply chain data (capacities, lead-times, network relationships, etc.) and up to date transaction data (order status, inventory positions, shipment tracking, etc.) Figure 2: Results Achieved Using Big Data Analytics.

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Collaboration Technology for a More Sustainable Supply Chain

Turvo

Unified data enables analytics to create efficiency and reduce emissions in several ways. Analytics platforms monitor data generated by day-to-day logistics activities. This includes analyzing volumes, distances, and mode selections, and documenting inefficient modes, routing, and empty miles that flow from poor utilization.

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Exception management: An important aspect of supply chain visibility

20Cube Logistics

A reputed organisation’s study says that 71% of global CIOs believe the primary function of their supply chain by 2020 will be more of a customer service driver, so the ability to better manage exceptions becomes extremely essential for every company. Managing by exception is nothing but data management challenges.

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The Intelligent IBP Manifesto – key principles for a new planning paradigm

Supply Chain Trend

Principle 9: Intelligent IBP maintains a digitized library of decisions, how they are made, what the common options are, what data inputs and analytics are required and who decides. The options to choose from will largely be known, so are the data, plans & insights required. Decision quality will become a performance metric in IBP.

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Business Developer Analyst

Claritum

Responsible for reporting data management . Extract, Transform & Loading data into data warehouse from production systems. Change data capture. Database extraction, analysis and testing as part of data assurance. Recommends quality metrics and ensures quality metrics are documented and tracked.

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ERP Master Data: A Guide to Improve Quality & Governance

SCMDOJO

[Read More: 6 ERP Implementation Failure Reasons ] Enterprise resource planning applications are the foundation of any data-driven company, but tools for data quality management, master data management, and data workflow can help you streamline your processes and manage your master data more effectively.