Supply Chain Analytics

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.

Supply chain organizations can vary based on their functions, structures, and industries. They work together to move products from raw materials to finished goods in the hands of consumers. Each supply chain organization plays a distinct role, but they all contribute to the larger goal of meeting customer demand in a timely and cost-effective manner. Supply chain organizations include Manufacturers, Suppliers, Distributors or Wholesalers, Retailers, Logistics Providers, Freight Forwarders, Transportation Carriers, Fourth-Party Logistics, E-Commerce Platforms, Customs Brokers, Procurement Organizations, and Reverse Logistics Providers.

Supply chains are used in every industry where goods or services are produced, sold, or consumed. These industries include but are not limited to Manufacturing, Retail, Pharmaceutical and Healthcare, Food and Beverage, Aerospace and Defense, Construction, Energy, Technology and Electronics, Apparel and Fashion, Automotive, Chemical, Telecommunications, Mining and Metals, Logistics and Transportation, Hospitality and Tourism, Entertainment, Agriculture and Farming, Textile, Financial Services, and Education and Publishing.

Supply chain activities encompass all the processes involved in the flow of goods and services, from the initial procurement of raw materials to the final delivery of products to consumers. These activities ensure that materials, information, and finances move efficiently through the supply chain. The main supply chain activities include Procurement, Manufacturing or Production, Inventory Management, Warehousing, Transportation and Logistics, Order Fulfillment, Demand Planning and Forecasting, Supplier Relationship Management, Customer Service and Returns Management, and Sustainability and Compliance, among others.

Supply Chain Analytics 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 by 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 by 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 by 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 by building on descriptive, 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.