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Título de Acceso Abierto
Supply Chain Analytics
Resumen/Descripción – provisto por la editorial en inglés
Firms from all industries operate within complex global supply chains since today's business activities are fragmented among many dispersed partners. These supply chains are fragile and exposed to various risks and threats, requiring advanced risk management and resilience competencies. Data is the lifeblood of a supply chain. Digitization and analytics are vital in monitoring real-time data, predicting future patterns, and quickly responding to unforeseen events. Supply chain analytics can help companies adapt in real-time to shifting customer demand caused by disruptions. Analytics can drive significant operational efficiencies by providing visibility into supply chains. Supply chain analytics collects, analyzes, and synthesizes data to provide insights into supply chain performance. Supply chain managers must use data and analytics to transform their supply chain into a robust and resilient supply chain and create more opportunities to remain competitive and diversified. Future supply chain managers should be digitally savvy. They will be storytellers with the skills to dig into the countless layers of supply chain data to transform data into insight and make informed decisions. The principal objective of Supply Chain Analytics is to provide state-of-the-art information for academic researchers, policymakers, and practitioners concerned with developing new methodologies and technologies to formulate and solve supply chain problems. The journal is a source of information for theoretical, empirical, and analytical research, real-world applications, and case studies in supply chain management and analytics. The journal covers: Descriptive supply chain analytics: applying statistical models to understand a supply chain's past and current data and display it with charts and graphs to answer questions about the current health of a supply chain. Descriptive analytics can show what has happened and what is happening by analyzing supply chain data for trends and patterns. Diagnostic supply chain analytics: providing supply chain managers with the tools and technologies to discover problems in supply chains. It uses in-depth data mining and correlation analysis to answer why something happens. Diagnostic analytics can be used to understand data anomalies and explain deviations from expectations and norms when paired with powerful visualization tools and technologies. Predictive supply chain analytics: focusing on the future. It applies complex forward-looking mathematical models such as artificial intelligence and machine learning to large amounts of historical data collected through descriptive analytics to help supply chain managers predict what will happen in the future. Prescriptive supply chain analytics: building on predictive and diagnostic analytics to compare scenarios, provide insight, and suggest alternative courses of action to supply chain managers. It uses sophisticated machine learning, optimization, and simulation methods and typically requires more data to anticipate various outcomes effectively and efficiently.Palabras clave – provistas por la editorial
No disponibles.
Disponibilidad
| Institución detectada | Período | Navegá | Descargá | Solicitá |
|---|---|---|---|---|
| No requiere | desde jun. 2024 / hasta jun. 2024 | ScienceDirect |
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Información
Tipo de recurso:
revistas
ISSN electrónico
2949-8635
